Phase I report analysis
Maude David & Shoko Iwai and updated by Austin Martin
2020-08-10
1 Setup
1.1 Load libraries
## Warning: package 'tibble' was built under R version 3.6.3## Warning: package 'vegan' was built under R version 3.6.3## Warning: package 'DESeq2' was built under R version 3.6.3## Warning: package 'phyloseq' was built under R version 3.6.31.2 All parameters
1.2.1 File paths
##### If you have M3 google drive and bitbucket repo on your local machine,
##### you only need to change two parameters below
# M3-shared google drive location
#M3folder_loc_for_ps='/Google Drive/M3-shared/V4/Data/200312_ASVdata8_updateAsteria/ps_notnorm_age_filter_complete_family.rds'
# Loading ps using actual filepath for analysis (To change depending on the user)
ps_not_norm_comp <- readRDS("~/M3_Datasets/ps_not_norm_age_filter_complete_family.rds")
## Output data location (subject to change)
#output_data=paste0(M3folder_loc, 'Data/V4/180808_ASVdata4/OutputData_Agefiltered/')
output_data <- "~/M3_Datasets/"1.2.2 Cutoff parameters
# min post DADA2 counts to run analysis
min_postDD=20000
# DESeq significant cutoff
deseq_cut=0.05
# metagenomeSeq significant cutoff
mtgseq_cut=0.05
# chisquare test cutoff (for diet questionnare results significance)
chisq_cut=0.05
# PERMANOVA pvalue and R2 cutoff for visualization
permanova_pcut=0.05
permanova_cut=0.12 Summarize metadata
# chisquared test function
run_chisq_test <- function(ps, metacol){
# ps: phyloseq object
# metacol: metadata column name to test
metaDF <- data.frame(sample_data(ps))
# remove NAs, for some reason, some NA are recorded as a text!
submetaDF=metaDF[!is.na(metaDF[, metacol]), ]
submetaDF=submetaDF[submetaDF[, metacol]!='NA', ]
submetaDF=submetaDF[submetaDF[, metacol]!='', ] # also remove blank
# chisquared test
chisq_res=chisq.test(table(submetaDF[, metacol], submetaDF[, 'phenotype']))
# extract results
resDT=data.table(chisq_res$observed)
# dcast for printing
resDT <- data.table(dcast(resDT, V1 ~ V2, value.var='N'))
resDT <- resDT[, testvar:=metacol]
resDT <- resDT[, chisq_p:=chisq_res$p.value]
return(resDT[, list(testvar, category=V1, A, N, chisq_p)])
}
# composition plot function
plot_composition <- function(chisq_resDT, var_name){
# chisq_resDT: 4 columns. testvar, category, A, N, chisq_p
plotDT=melt(chisq_resDT, id.vars=c('testvar', 'category', 'chisq_p'))
p=ggplot(data=plotDT[testvar==var_name], aes(x=variable, y=value, fill=category))+
geom_bar(stat="identity")+
xlab('')+ylab('Number of sample')+
ggtitle(var_name)+
theme_minimal()+
theme(legend.title=element_blank(), legend.position="bottom", axis.text.x=element_text(vjust=1, hjust=1))+
scale_fill_manual(values=sgColorPalette)
print(p)
}2.1 Demographics
2.2 fix metadata and remove samples that are too young or have diagnostic discrepancies
#Remove Breastfed and their families
breast_fed <- c("089_A","054_N", "158_N" )
b_fed<-unique(ps_not_norm_comp@sam_data$Family.group.ID[which(ps_not_norm_comp@sam_data$Host.Name %in% breast_fed)])
#remove the whole family
for (i in b_fed){
ps_not_norm_comp<- prune_samples(ps_not_norm_comp@sam_data$Family.group.ID != i, ps_not_norm_comp)
}
#fixing the mapping file for stats by adding categorical vs non catergorical
metadata_ok<-sample_data(ps_not_norm_comp)
write.csv(metadata_ok, "sam_data.csv")
map<-read.csv("sam_data.csv")
meta_cat <- read.csv("updated_metacategories.csv")
for (i in meta_cat$varname.16S.V4[which(meta_cat$permanova != FALSE)]){
if (meta_cat$permanova[which(meta_cat$varname.16S.V4 == i)] == "Categorical") {
map[,i] <- as.factor(map[,i])
} else {
map[,i] <- as.numeric(map[,i])
}
}
makeFieldsNumeric <- function(map){
handleNAs <- function(vec){
vec[vec == ""] <- "NA"
vec[is.na(vec)] <- "NA"
return(vec)
}
map$Stool.frequency <- handleNAs(as.character(map$Stool.frequency))
map$Stool.frequency[as.character(map$Stool.frequency) == "Less than 1"] = 0
map$Stool.frequency[as.character(map$Stool.frequency) == "5 or more"] = 5
map$Dairy..consumption.frequency...longitudinal.[map$Dairy..consumption.frequency...longitudinal. == 5] <- "3-4 meals per week"
#map$LR2[map$LR2 == "1 (ASD)"] = 1
#map$LR2[map$LR2 == "0 (non-ASD"] = 0
freq_dict_2 <- list("Never" = 0, "Rarely" = 1, "Occasionally" = 2, "Regularly" = 3, "Weekly" = 4, "weekly" = 4,
"Several time weekly" = 5, "Several times weekly" = 5, "Daily" = 6, "NA" = NA)
dict_2_items <- c("Whole.grain..consumption.frequency.", "Fermented.vegetable..consumption.frequency.", "Dairy..consumption.frequency.", "Fruit..consumption.frequency.", "Meals.prepared.at.home..consumption.frequency.", "Ready.to.eat.meals..consumption.frequency.", "Red.meat..consumption.frequency.", "Olive.oil.used.in.cooking..M3.", "Seafood..consumption.frequency.", "Sweetened.drink..consumption.frequency.", "Vegetable..consumption.frequency.",
"Restaurant.prepared.meals..consumption.frequency.", "Sugary.food..consumption.frequency.", "Probiotic..consumption.frequency.", "Vitamin.B.complex.supplement..consumption.frequency.", "Vitamin.D..consumption.frequency.")
for(item in dict_2_items){
print(item)
tmp <- rep(NA, nrow(map))
freqs <- handleNAs(map[,item])
numeric_rep <- unlist(freq_dict_2[freqs])
print(paste("Numeric rep length: ", length(numeric_rep)))
print(sum(!is.na(freqs)))
tmp[!is.na(freqs)] <- as.numeric(numeric_rep)
map[ , item] <- tmp
}
freq_dict_1 <- list("Never or less than once per week" = 0, "3-4 meals per week" = 1, "5" = 2, "7-10 meals per week" = 3, "Almost every meal" = 4, "NA" = NA)
dict_1_items <- c("Starchy.food..consumption.frequency...longitudinal.", "Meats.and.seafood..consumption.frequency...longitudinal.", "Bread..consumption.frequency...longitudinal.", "Dairy..consumption.frequency...longitudinal.", "Dietary.fat.and.oil..consumption.frequency...longitudinal.", "Vegetable..consumption.frequency...longitudinal.",
"Fruit..consumption.frequency...longitudinal.")
for(item in dict_1_items){
print(item)
tmp <- rep(NA, nrow(map))
freqs <- handleNAs(map[ , item])
numeric_rep <- unlist(freq_dict_1[freqs])
print(paste("Numeric rep length: ", length(numeric_rep)))
print(sum(!is.na(freqs)))
tmp[!is.na(freqs)] <- as.numeric(numeric_rep)
map[ , item] <- tmp
}
#may add more, but these variable only apply to phenotype for autism
freq_dict_2 <- list("Able to speak fluently" = 3,"Phrase speech"=2, "Single word speech"=1, "Little to no speech" = 0, "Able to have conversation" = 3, "Limited conversation ability" = 2, "Difficulty with conversation" = 1, "Cannot have a conversation" = 0,"Understands about half of words" = 1, "Understands few or no words"= 0, "Understands many words" = 2, "Understands most words"= 3, "Understands nearly all words" = 4 ,"NA" = NA)
dict_2_items <- c("Language.ability.and.use", "Conversation.ability", "Understands.speech")
for(item in dict_2_items){
print(item)
tmp <- rep(NA, nrow(map))
freqs <- handleNAs(map[ , item])
numeric_rep <- unlist(freq_dict_2[freqs])
print(paste("Numeric rep length: ", length(numeric_rep)))
print(sum(!is.na(freqs)))
tmp[!is.na(freqs)] <- as.numeric(numeric_rep)
map[ , item] <- tmp
}
map <- map[!duplicated(map$Biospecimen.Barcode), ]
rownames(map) <- map$Biospecimen.Barcode
map$Stool.frequency <- as.numeric(map$Stool.frequency)
return(map)
}
dict_1_items <- c("Starchy.food..consumption.frequency...longitudinal.", "Meats.and.seafood..consumption.frequency...longitudinal.", "Bread..consumption.frequency...longitudinal.", "Dairy..consumption.frequency...longitudinal.", "Dietary.fat.and.oil..consumption.frequency...longitudinal.", "Vegetable..consumption.frequency...longitudinal.",
"Fruit..consumption.frequency...longitudinal.")
dict_2_items <- c("Language.ability.and.use", "Conversation.ability", "Understands.speech")
map<-makeFieldsNumeric(map)## [1] "Whole.grain..consumption.frequency."
## [1] "Numeric rep length: 447"
## [1] 447
## [1] "Fermented.vegetable..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Dairy..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Fruit..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Meals.prepared.at.home..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Ready.to.eat.meals..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Red.meat..consumption.frequency."
## [1] "Numeric rep length: 78"
## [1] 78
## [1] "Olive.oil.used.in.cooking..M3."
## [1] "Numeric rep length: 447"
## [1] 447
## [1] "Seafood..consumption.frequency."
## [1] "Numeric rep length: 447"
## [1] 447
## [1] "Sweetened.drink..consumption.frequency."
## [1] "Numeric rep length: 447"
## [1] 447
## [1] "Vegetable..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Restaurant.prepared.meals..consumption.frequency."
## [1] "Numeric rep length: 447"
## [1] 447
## [1] "Sugary.food..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Probiotic..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Vitamin.B.complex.supplement..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Vitamin.D..consumption.frequency."
## [1] "Numeric rep length: 450"
## [1] 450
## [1] "Starchy.food..consumption.frequency...longitudinal."
## [1] "Numeric rep length: 421"
## [1] 421
## [1] "Meats.and.seafood..consumption.frequency...longitudinal."
## [1] "Numeric rep length: 421"
## [1] 421
## [1] "Bread..consumption.frequency...longitudinal."
## [1] "Numeric rep length: 421"
## [1] 421
## [1] "Dairy..consumption.frequency...longitudinal."
## [1] "Numeric rep length: 421"
## [1] 421
## [1] "Dietary.fat.and.oil..consumption.frequency...longitudinal."
## [1] "Numeric rep length: 421"
## [1] 421
## [1] "Vegetable..consumption.frequency...longitudinal."
## [1] "Numeric rep length: 421"
## [1] 421
## [1] "Fruit..consumption.frequency...longitudinal."
## [1] "Numeric rep length: 421"
## [1] 421
## [1] "Language.ability.and.use"
## [1] "Numeric rep length: 225"
## [1] 225
## [1] "Conversation.ability"
## [1] "Numeric rep length: 225"
## [1] 225
## [1] "Understands.speech"
## [1] "Numeric rep length: 225"
## [1] 225map_levels<-sapply(map, levels)
map_levelscount<-sapply(map_levels, length)
mapnotfac <- names(map_levelscount[which(map_levelscount >= 18)])
for (i in mapnotfac){
map[,i]<-as.character(map[,i])
}
#Round years
map$Age..years. <-round(map$Age..years.)
sample_data(ps_not_norm_comp) <- map
#remove samples that are too young and their paired sibling
tooyoung <-ps_not_norm_comp@sam_data$Family.group.ID[which(ps_not_norm_comp@sam_data$Age..months. < 24)]
#family 65 should be removed according to >24 months criteria
ps_not_norm_comp<- prune_samples(ps_not_norm_comp@sam_data$Family.group.ID != unique(tooyoung), ps_not_norm_comp)
#List of individuals that were reported w/ autism, but was not classified as such through MARA and/or video classifier
pheno_contrad <-read.csv("phenotype_contradictions.csv")
contradicting<-unique(ps_not_norm_comp@sam_data$Family.group.ID[which(ps_not_norm_comp@sam_data$Host.Name %in% as.character(pheno_contrad$host_name))])
#remove the whole family
for (i in contradicting){
ps_not_norm_comp<- prune_samples(ps_not_norm_comp@sam_data$Family.group.ID != i, ps_not_norm_comp)
}
#round off year
ps_not_norm_comp@sam_data$Age..years.<-round(ps_not_norm_comp@sam_data$Age..years.)
metadata_ok<-ps_not_norm_comp@sam_data2.2.1 Biological.sex
#now let's only run the categorical values for chi square and remove the first column which are not metadata
num_cat<-names(Filter(is.numeric, metadata_ok))
fac_cat<-names(Filter(is.factor, metadata_ok))
#removing the first 13 columns, since it's not metadata and the last one which is phenotype
fac_cat<-fac_cat[-c(1:13, length(fac_cat))]
#finally remiving the ones that were only asked for the children with ASD, or only have one factor & NA, or only present in one phen
fac_cat<-fac_cat[-which(fac_cat %in% c("Behavior.video.submitted..M3.","Language.ability.and.use","Conversation.ability","Understands.speech","Plays.imaginatively.when.alone","Plays.imaginatively.with.others","Plays.in.a.group.with.others","Eye.contact.finding","Childhood.behavioral.development.finding","Repetitive.motion","Picks.up.objects.to.show.to.others","Sleep.pattern.finding","Response.to.typical.sounds","Self.injurious.behavior.finding","Gastrointestinal.problems..M3.", "Imitation.behavior", "Other.stool.sample.collection.method.explained..M3.", "Flu.shot.in.the.last..MFlu.shot.in.the.last..M3.", "Pica.disease", "Additional.info.affecting.microbiome..M3.", "Dietary.restrictions.details..M3.", "Pet.bird"))]
#Also remove the ones with only one factor (no chi-square possible)
#now running the chisquare on all categorical values
chisquare_p.val=c()
names_chisquare_p.val=c()
all_chisquare=list()
chi_list<-names(map_levelscount)[map_levelscount > 1]
chi_list <-chi_list[-c(1:9)]
chi_list<-chi_list[-which(chi_list %in% c("Behavior.video.submitted..M3.","Language.ability.and.use","Conversation.ability","Understands.speech","Plays.imaginatively.when.alone","Plays.imaginatively.with.others","Plays.in.a.group.with.others","Eye.contact.finding","Childhood.behavioral.development.finding","Repetitive.motion","Picks.up.objects.to.show.to.others","Sleep.pattern.finding","Response.to.typical.sounds","Self.injurious.behavior.finding","Gastrointestinal.problems..M3.", "Imitation.behavior", "Other.stool.sample.collection.method.explained..M3.", "Flu.shot.in.the.last..MFlu.shot.in.the.last..M3.", "Pica.disease", "Additional.info.affecting.microbiome..M3.", "Dietary.restrictions.details..M3.", "Pet.bird", "Host.disease.status", "phenotype"))]
map_num<-sapply(map, is.numeric)
num_cat <- colnames(map[,as.vector(which(map_num == TRUE))])
num_cat <- num_cat[-c(1:4)]
num_cat <- num_cat[-which(num_cat %in% c("Language.ability.and.use", "Conversation.ability", "Understands.speech", "Mobile.Autism.Risk.Assessment.Score", "Number.of.small.pet.herbivores", "Number.of.small.pet.rodents", "phenotype_num", "Number.of.pet.birds"))]
#Run chi on all
chi_list <- c(chi_list, num_cat)
for (i in 1:length(chi_list)){
tmp<-run_chisq_test(ps_not_norm_comp, chi_list[i])
chisquare_p.val<-c(chisquare_p.val,min(tmp$chisq_p))
names_chisquare_p.val<-c(names_chisquare_p.val,chi_list[i])
all_chisquare[[i]]<-tmp
}
names(chisquare_p.val)<-names_chisquare_p.val
names(all_chisquare) <-chi_list
# p-value correction
chisquare_p.val<-p.adjust(chisquare_p.val)
chisquare_p.val<-chisquare_p.val[chisquare_p.val < 0.05]
length(chisquare_p.val) #41## [1] 37chisquare_p.val## Functional.bowel.finding
## 1.789615e-07
## Dietary.regime
## 9.029215e-03
## GI.symptoms.within.3.months..M3.
## 4.521062e-14
## Biological.sex
## 3.976313e-07
## GI.issues.this.week..M3.
## 3.552499e-10
## Non.celiac.gluten.sensitivity
## 1.134098e-04
## Lactose.intolerance
## 1.321945e-02
## Dietary.restrictions..M3.
## 2.003499e-07
## Dietary.supplement
## 1.915985e-08
## LR6.prediction..M3.
## 6.539853e-15
## LR10.prediction..M3.
## 2.079463e-11
## LR5.prediction..M3.
## 7.924848e-16
## Toilet.trained
## 1.540770e-02
## Other.symptoms.this.week..M3.
## 3.743232e-02
## Recent.anxiety..caretaker.reported.
## 1.106379e-03
## Toilet.cover..M3.
## 1.530253e-03
## Most.recent.GI.episode.symptoms..M3.
## 2.423190e-04
## Stool.frequency
## 4.117528e-04
## Dairy..consumption.frequency.
## 9.851763e-07
## Fruit..consumption.frequency.
## 2.390145e-08
## Meals.prepared.at.home..consumption.frequency.
## 1.846027e-02
## Vegetable..consumption.frequency.
## 2.685047e-06
## Age..months.
## 5.262075e-15
## Probiotic..consumption.frequency.
## 3.155060e-08
## Vitamin.B.complex.supplement..consumption.frequency.
## 2.663542e-08
## Vitamin.D..consumption.frequency.
## 2.765978e-08
## LR6.probability.not.ASD..M3.
## 2.156944e-10
## LR6.probability.ASD..M3.
## 2.156944e-10
## LR10.probability.not.ASD..M3.
## 2.156944e-10
## LR10.probability.ASD..M3.
## 2.156944e-10
## LR5.probability.not.ASD..M3.
## 1.783339e-12
## LR5.probability.ASD..M3.
## 1.783339e-12
## Age..years.
## 1.293926e-14
## Bread..consumption.frequency...longitudinal.
## 4.062774e-04
## Dairy..consumption.frequency...longitudinal.
## 1.815090e-05
## Vegetable..consumption.frequency...longitudinal.
## 4.698634e-05
## Fruit..consumption.frequency...longitudinal.
## 1.134098e-04#vizualisation of the results
#select only the signififcant ones
all_chisquare<-all_chisquare[names(all_chisquare) %in% names(chisquare_p.val)]
#save this into a csv
write.csv(format(chisquare_p.val, digits=2), file=paste0(output_data,"Xsqr_05.csv"), quote=F)
# plot one example out of 29
plot_composition(all_chisquare[1], names(all_chisquare)[1])
2.2.2 Racial.group
# print number table
table(sample_data(ps_not_norm_comp)$Racial.group, sample_data(ps_not_norm_comp)$Biological.sex)##
## Female Male
## Asian race 12 30
## Asian race & Middle Eastern race 3 3
## Asian race & Unknown racial group 3 3
## Caucasian 81 165
## Caucasian & Hispanic 0 12
## Caucasian & Indian (subcontinent) race 0 6
## Hispanic 6 18
## Hispanic & African race 6 0
## Indian (subcontinent) race 3 3
## Indian race 6 6
## Unknown racial group 0 12# run chisquared test
race=run_chisq_test(ps_not_norm_comp, 'Racial.group')
# print results
pander(race)| testvar | category | A | N | chisq_p |
|---|---|---|---|---|
| Racial.group | Asian race | 21 | 21 | 1 |
| Racial.group | Asian race & Middle Eastern race | 3 | 3 | 1 |
| Racial.group | Asian race & Unknown racial group | 3 | 3 | 1 |
| Racial.group | Caucasian | 123 | 123 | 1 |
| Racial.group | Caucasian & Hispanic | 6 | 6 | 1 |
| Racial.group | Caucasian & Indian (subcontinent) race | 3 | 3 | 1 |
| Racial.group | Hispanic | 12 | 12 | 1 |
| Racial.group | Hispanic & African race | 3 | 3 | 1 |
| Racial.group | Indian (subcontinent) race | 3 | 3 | 1 |
| Racial.group | Indian race | 6 | 6 | 1 |
| Racial.group | Unknown racial group | 6 | 6 | 1 |
# plot
plot_composition(race, 'Racial.group')
# % table
race_prop=prop.table(as.matrix(race[, .(A, N)]), margin=2)*100
row.names(race_prop) <- race$category
pander(race_prop)| Â | A | N |
|---|---|---|
| Asian race | 11.11 | 11.11 |
| Asian race & Middle Eastern race | 1.587 | 1.587 |
| Asian race & Unknown racial group | 1.587 | 1.587 |
| Caucasian | 65.08 | 65.08 |
| Caucasian & Hispanic | 3.175 | 3.175 |
| Caucasian & Indian (subcontinent) race | 1.587 | 1.587 |
| Hispanic | 6.349 | 6.349 |
| Hispanic & African race | 1.587 | 1.587 |
| Indian (subcontinent) race | 1.587 | 1.587 |
| Indian race | 3.175 | 3.175 |
| Unknown racial group | 3.175 | 3.175 |
# write
write.csv(race_prop, file=paste0(output_data, 'Race.csv'))2.2.3 Age..months.
# make sure it is numeric
sample_data(ps_not_norm_comp)$Age..months. <- as.numeric(sample_data(ps_not_norm_comp)$Age..months.)
# plot
ggplot(data=sample_data(ps_not_norm_comp), aes(x=phenotype, y=Age..months., fill=phenotype))+
geom_boxplot(width=0.7, outlier.colour='white')+
geom_jitter(size=1, position=position_jitter(width=0.1))+
xlab('')+ylab('Age (months)')+
scale_fill_manual(values=sgColorPalette)+
theme_minimal()
# run tests to check significance
shapiro.test(sample_data(ps_not_norm_comp)$Age..months.) #not normal we need a reanking test##
## Shapiro-Wilk normality test
##
## data: sample_data(ps_not_norm_comp)$Age..months.
## W = 0.96494, p-value = 7.196e-08wilcox.test(Age..months. ~ phenotype, data=data.frame(sample_data(ps_not_norm_comp)), var.equal=FALSE)##
## Wilcoxon rank sum test with continuity correction
##
## data: Age..months. by phenotype
## W = 20889, p-value = 0.004351
## alternative hypothesis: true location shift is not equal to 0#Let's generalized all the values with numeric input
wilcox_pval=c()
for (i in 1:length(num_cat)){
#if (levels(get(num_cat[i], metadata_ok)) >= 2)
tmp<-wilcox.test(get(num_cat[i]) ~ phenotype, data=map, var.equal=FALSE)
wilcox_pval<-c(wilcox_pval,tmp$p.value)
}
names(wilcox_pval)<-num_cat
#correction
wilcox_pval<-p.adjust(wilcox_pval)
wilcox_pval[wilcox_pval<0.05] #LRprobabilities , Veget, bread, dairy fruit, sweet drink## Seafood..consumption.frequency.
## 3.362736e-02
## Vegetable..consumption.frequency.
## 3.153965e-02
## Age..months.
## 9.539155e-03
## LR6.probability.not.ASD..M3.
## 5.653878e-20
## LR6.probability.ASD..M3.
## 5.653878e-20
## LR10.probability.not.ASD..M3.
## 4.598240e-22
## LR10.probability.ASD..M3.
## 4.598240e-22
## LR5.probability.not.ASD..M3.
## 9.546453e-19
## LR5.probability.ASD..M3.
## 9.546453e-19
## Age..years.
## 5.614868e-03
## Bread..consumption.frequency...longitudinal.
## 8.026472e-05
## Dairy..consumption.frequency...longitudinal.
## 2.591744e-05
## Vegetable..consumption.frequency...longitudinal.
## 3.184748e-02
## Fruit..consumption.frequency...longitudinal.
## 3.043812e-032.3 Dietary variance
Dietary variance amongst ASD patients will also be assessed. Based on preliminary analyses, we expect that ASD participants, collectively, will exhibit a minimal degree of dietary variance.
# read metadata category file
#meta_cat=fread(paste0(M3folder_loc, meta_cat_fp), header=TRUE)
#Created this csv from the file in the shared M3 google drive, took the first sheet and removed the last incomplete column, then converted to csv
meta_cat<-read.csv("updated_metacategories.csv")
colnames(meta_cat)[1] <- "varname"
# list of diet questions
diet_info<-metadata_ok[,colnames(metadata_ok) %in% meta_cat$varname[which(meta_cat$diet==TRUE)]]
#additionnal error to remove: filled with only NA or one factor, cant do chisquare on one factor
diet_col_levels<-sapply(diet_info, levels)
dietcol_levelscount<-sapply(diet_col_levels, length)
#Since there numerics dietary variables are only two and filled primarily with NAs (as shown by line 248, we will omit)
#diet_info[,which(sapply(diet_info, class) == "numeric")]
diet_info <- diet_info[,which(dietcol_levelscount >= 2)]
#dietq_col <-which(colnames(sample_data(ps_not_norm_comp)) %in% colnames(diet_info))
dietq_col <- colnames(diet_info)
# for each variable, summarize and check if A vs N different? (we hypothesized variance in ASD are the same as NT?)
master_resDT=NULL
for(i in dietq_col){
resDT=run_chisq_test(ps_not_norm_comp, i)
# add to master
master_resDT <- rbindlist(list(master_resDT, resDT))
}
# variables tested
unique(master_resDT$testvar)## [1] "Dietary.regime" "Meat..consumption.frequency."
## [3] "Multivitamin" "Dietary.supplement"# order by significance
master_resDT <- master_resDT[order(chisq_p)]
# print table
datatable(master_resDT)# write csv file
write.csv(master_resDT, file=paste0(output_data, 'Dietary_var_all_proportion.csv'))
write.csv(unique(master_resDT[, list(testvar, chisq_p)]), file=paste0(output_data, 'Dietary_var_chisq_test.csv'))
# plot top 3 most significant vars
plot_diet=master_resDT[testvar %in% unique(master_resDT[, list(testvar, chisq_p)])$testvar[1:3]]
for(i in unique(plot_diet$testvar)){
plot_composition(plot_diet, i)
}
3 Normalization
dir.create(paste0(output_data, 'Normalized/'))
#Filtering of the prevalence:
###Declare function to filter
filterTaxaByPrevolence <- function(ps, percentSamplesPresentIn){
prevalenceThreshold <- percentSamplesPresentIn * nsamples(ps)
toKeep <- apply(data.frame(otu_table(ps)), 1, function(taxa) return(sum(taxa > 0) > prevalenceThreshold))
ps_filt <- prune_taxa(toKeep, ps)
return(ps_filt)
}
#CSS norm function
#We actually will plot everything with CSS
CSS_norm<-function(ps){
ps.metaG<-phyloseq_to_metagenomeSeq(ps)
p_stat = cumNormStatFast(ps.metaG)
ps.metaG = cumNorm(ps.metaG, p = p_stat)
ps.metaG.norm <- MRcounts(ps.metaG, norm = T)
ps_CSS<-phyloseq(otu_table(ps.metaG.norm, taxa_are_rows = T), sample_data(ps),tax_table(ps))
return(ps_CSS)
}
#Deseq norm
deSeqNorm <- function(ps){
ps_dds <- phyloseq_to_deseq2(ps, ~ phenotype)
ps_dds <- estimateSizeFactors(ps_dds, type = "poscounts")
ps_dds <- estimateDispersions(ps_dds)
abund <- getVarianceStabilizedData(ps_dds)
abund <- abund + abs(min(abund)) #don't allow deseq to return negative counts
ps_deSeq <- phyloseq(otu_table(abund, taxa_are_rows = T), sample_data(ps), tax_table(ps))
return(ps_deSeq)
}
#Now we remove the taxa present in less than 3 % of the samples with some basic filtering
filtered_ps003<-filterTaxaByPrevolence(ps_not_norm_comp, 0.03)
filtered_ps003## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 858 taxa and 378 samples ]
## sample_data() Sample Data: [ 378 samples by 365 sample variables ]
## tax_table() Taxonomy Table: [ 858 taxa by 8 taxonomic ranks ]saveRDS(filtered_ps003, file=paste0(output_data, "Normalized/ps_not_norm_comp_pass_min_postDD_min0.03.Rda"))
# CSS normalization
ps_CSS_norm_pass_min_postDD_sup003<-CSS_norm(filtered_ps003)
saveRDS(ps_CSS_norm_pass_min_postDD_sup003, file=paste0(output_data, "Normalized/ps_CSS_pass_min_postDD_min0.03.Rda"))
# DESeq normalization
ps_DeSeq_norm_pass_min_postDD_sup003<-deSeqNorm(filtered_ps003)
saveRDS(ps_DeSeq_norm_pass_min_postDD_sup003, file=paste0(output_data, "Normalized/ps_DeSeq_pass_min_postDD_min0.03.Rda"))
# TSS normalization
propDF=prop.table(as.matrix(otu_table(filtered_ps003)), margin=2)
ps_TSS_norm_pass_min_postDD_sup003 <- phyloseq(otu_table(propDF, taxa_are_rows=TRUE),
tax_table(filtered_ps003),
sample_data(filtered_ps003))3.0.1 Repeated measures ANOVA to find ASVs that have means that change significantly between timepoints
#format asv table with timepoint + hostname info
asv_table<-t(otu_table(ps_DeSeq_norm_pass_min_postDD_sup003))
asv_table <- as.data.frame(asv_table)
asv_table$timepoint <- ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Within.study.sampling.date
asv_table$timepoint <- as.factor(asv_table$timepoint)
asv_table$Host.Name <- ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Host.Name
#Create initial table of first asv to build off of
tmp <-summary(aov(asv_table[,1] ~ timepoint + Error(Host.Name/timepoint), data = asv_table))
p_val <-tmp$`Error: Host.Name:timepoint`[[1]]$`Pr(>F)`[1]
anova_asv_res <- tibble(colnames(asv_table[1]), p_val)
colnames(anova_asv_res) <- c("ASV", "p_val")
#Run for each asv
for (i in 2:(length(colnames(asv_table))-2)) {
form <-as.formula(paste0("asv_table[," , i, "]", " ~ timepoint + Error(Host.Name/timepoint)"))
tmp <-summary(aov(formula = form, data = asv_table))
p_val <-tmp$`Error: Host.Name:timepoint`[[1]]$`Pr(>F)`[1]
tmpres <- tibble(colnames(asv_table[i]), p_val)
colnames(tmpres) <- c("ASV", "p_val")
anova_asv_res<- rbind(anova_asv_res, tmpres)
}
#find sig ones btwn timepoints
asv_sig_btwn_timep_DES<-anova_asv_res$ASV[which(anova_asv_res$p_val <= 0.05)]
asv_sig_btwn_timep_DES## [1] "GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCGCGTAGGTGGTTCAGCAAGTTGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCCAAAACTACTGAGCTAGAGTACGGTAGAGGGTGGTGGAATTTCCTGTGTAGCGGTGAAATGCGTAGATATAGGAAGGAACACCAGTGGCGAAGGCGACCACCTGGACTGATACTGACACTGAGGTGCGAAAGCGTGGGG"
## [2] "GCAAGCGTTATCCGGAATCATTGGGCGTAAAGGGTGCGTAGGTGGCGTACTAAGTCTGTAGTAAAAGGCAATGGCTCAACCATTGTAAGCTATGGAAACTGGTATGCTAGAGTGCAGAAGAGGGCGATGGAATTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAGGAACACCAGTGGCGAAGGCGGTCGCCTGGTCTGTAACTGACACTGAGGCACGAAAGCGTGGGG"
## [3] "GCAAGCGTTATCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGATATTTAAGTGGGATGTGAAATACCTGAGCTTAACTTGGGAGCTGCATTCCAAACTGGATATCTAGAGTGCAGGAGAGGAGAATGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGATTCTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [4] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGCATGATAAGTCTGATGTGAAAACCCAAGGCTCAACCATGGGACTGCATTGGAAACTGTCGTGCTAGAGTGTCGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATGACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [5] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG"
## [6] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTCTGGCAAGTCTGATGTGAAAATCCGGGGCTCAACTCCGGAACTGCATTGGAAACTGTCAGACTAGAGTGTCGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [7] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCAGTGCAAGTCTGAAGTGAAAGCCTGGGGCTCAACCCCGGAACTGCTTTGGAAACTGTGCTGCTTGAGTACCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [8] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCAGTGCAAGTCTGAAGTGAAAGGCTGGGGCTCAACCCCGGAACTGCTTTGGAAACTGTGCTGCTTGAGTACCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [9] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGATGCAAGTCTGGAGTGAAAGCCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTATGGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [10] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGCGGTATGGCAAGTCTGATGTGAAAGGCCGGGGCTCAACCCCGGGACTGCATTGGAAACTGCCAGACTAGAGTGTCGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGACAACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [11] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGCGGTTATGCAAGTCCGATGTGAAAGCCCGGGGCTTAACCCCGGGACTGCATTAGAAACTGTGTAACTAGAGTGTCGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [12] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTATGGCAAGTCAGAAGTGAAAGGCTGGGGCTCAACCCCGGGACTGCTTTTGAAACTGTCAAACTAGAGTACAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGAAACTGACACTGAGGCACGAAAGCGTGGGG"
## [13] "GCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCTCGGCTTAACCGAGGAAGTGCATCGGAAACTGGGAAACTTGAGTACAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGT"
## [14] "GCAAGCGTTATCCGGATTTATTGGGTGTAAAGGGTGCGTAGACGGGAAGGTAAGTTAGTTGTGAAATCCCTCGGCTCAACTGAGGAACTGCGACTAAAACTGCTTTTCTTGAGTGCTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGACAGCAACTGACGTTGAGGCACGAAAGTGTGGGG"
## [15] "GCAAGCGTTGTCCGGAATAATTGGGCGTAAAGGGCGCGTAGGCGGCTCGGTAAGTCTGGAGTGAAAGTCCTGCTTTTAAGGTGGGAATTGCTTTGGATACTGTCGGGCTTGAGTGCAGGAGAGGTTAGTGGAATTCCCAGTGTAGCGGTGAAATGCGTAGAGATTGGGAGGAACACCAGTGGCGAAGGCGACTAACTGGACTGTAACTGACGCTGAGGCGCGAAAGTGTGGGG"
## [16] "GCAAGCGTTGTCCGGAATTACTGGGCGTAAAGGGTGCGTAGGCGGCCGTTTAAGTCAGATGTGAAATACCCGTGCTTAACATGGGGGCTGCATTTGAAACTGGATGGCTTGAGTGCAGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTGCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG"
## [17] "GCAAGCGTTGTCCGGAATTACTGGGCGTAAAGGGTGCGTAGGCGGCGCGTTAAGTCAGATGTGAAATACCCGTGCTTAACATGGGGGGTGCATCTGATACTGGCGTGCTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG"
## [18] "GCAAGCGTTGTCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGCCATGTAAGTCAGGTGTGAAAGACCGGGGCTCAACCCCGGGGTTGCACTTGAAACTGTGTGGCTTGAGTACAGGAGAGGGAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG"
## [19] "GCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAGCAAGTTGGAAGTGAAATCTGTGGGCTCAACTCACAAATTGCTTTCAAAACTGTTTTTCTTGAGTGGTGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACTAACTGACGCTGAGGCTCGAAAGCATGGGT"
## [20] "GCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGTGATCAAGTCAGCTGTGAAAACTACGGGCTTAACCCGTAGACTGCAGTTGAAACTGTTCATCTTGAGTGAAGTAGAGGTTGGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCGGTGGCGAAGGCGGCCAACTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG"
## [21] "GCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGTAGGCGGGGAGGCAAGTTGAATGTCTAAACTATCGGCTCAACTGATAGTCGCGTTCAAAACTGCCACTCTTGAGTGCAGTAGAGGTAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGT"
## [22] "GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGAGTACGTAGGCGGTTTGCTAAGCGCAAGGTGAAAGGCAGTGGCTTAACCATTGTAAGCCTTGCGAACTGACAGACTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGTACGAAAGCGTGGGG"
## [23] "GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGTTTCATAAGTCTGTCTTAAAAGTGCGGGGCTTAACCCCGTGAGGGGATGGAAACTATGGAACTGGAGTATCGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCCAGGGG"
## [24] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGACGGGTTTGCAAGTCAGATGTGAAATACCGCAGCTTAACTGCGGGGCTGCATTTGAAACTGCAAATCTTGAGTGCGGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACCGTAACTGACGTTGAGGCGCGAAAGCGTGGGT"
## [25] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGAGAGACAAGTCAGATGTGAAATCCGCGGGCTCAACCCGCGAACTGCATTTGAAACTGTTTCCCTTGAGTATCGGAGAGGTAACCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGGTTACTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG"
## [26] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGATCTGCAAGTCAGGCGTGAAATCCATGGGCTTAACCCATGAACTGCGCTTGAAACTGTGGGTCTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGTGTGGGT"
## [27] "GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGAAGAATAAGTCTGATGTGAAAGCCCTCGGCTTAACCGAGGAACTGCATCGGAAACTGTTTTTCTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGT"
## [28] "GCGAGCGTTATCCGGAATTATTGGGCGTAAAGAGTACGTAGGCGGTTTTTTAAGCGAGGGGTATAAGGCAGCGGCTTAACTGCTGTTGGCCCCTCGAACTGGAGGACTTGAGTGTCGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACGACAACTGACGCTGAGGTACGAAAGCGTGGGG"
## [29] "GCGAGCGTTATCCGGAATTATTGGGTGTAAAGGGTGCGTAGGCGGGATGTAAAGTCAGATGTGAAATGCCGCGGCTCAACCGCGGAGCTGCATTTGAAACTTATGTTCTTGAGTGAAGTAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAGGCGGCTTACTGGGCTTAGACTGACGCTGAGGCACGAAAGTGTGGGG"
## [30] "GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGCTTTTAAGTCAGCGGTCAAATGTCACGGCTCAACCGTGGCCAGCCGTTGAAACTGCAAGCCTTGAGTCTGCACAGGGCACATGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATCGCGAAGGCATTGTGCCGGGGCATAACTGACGCTGAGGCTCGAAAGTGCGGGT"
## [31] "GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGCGCGTAGGCGGGACGTCAAGTCAGCGGTAAAAGACTGCAGCTAAACTGTAGCACGCCGTTGAAACTGGCGCCCTCGAGACGAGACGAGGGAGGCGGAACAAGTGAAGTAGCGGTGAAATGCATAGATATCACTTGGAACCCCGATAGCGAAGGCAGCTTCCCAGGCTCGATCTGACGCTGATGCGCGAGAGCGTGGGT"
## [32] "GCGAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGTAGGCGGGATAGCAAGTCAGATGTGAAAACTATGGGCTCAACCTGTAGATTGCATTTGAAACTGTTGTTCTTGAGTGAAGTAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACATCGGTGGCGAAGGCGGCTTACTGGGCTTTTACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [33] "GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCTTGTAGGTGGCTGGTTGCGTCTGTCGTGAAAGCTCATGGCTTAACTGTGGGTTTGCGGTGGGTACGGGCTGGCTTGAGTGCAGTAGGGGAGGCTGGAATTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAGGAATACCGGTGGCGAAGGCGGGTCTCTGGGCTGTTACTGACACTGAGGAGCGAAAGCATGGGG"
## [34] "GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGGAGTGAAAGGCTACGGCTTAACCGTAGTAAGCTCTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTTTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG"
## [35] "t__217134"
## [36] "t__262755"
## [37] "t__264494"
## [38] "t__266763"
## [39] "t__520"#format asv table with timepoint + hostname info (CSS now)
asv_table<-t(otu_table(ps_CSS_norm_pass_min_postDD_sup003))
asv_table <- as.data.frame(asv_table)
asv_table$timepoint <- ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Within.study.sampling.date
asv_table$timepoint <- as.factor(asv_table$timepoint)
asv_table$Host.Name <- ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Host.Name
#Create initial table of first asv to build off of
tmp <-summary(aov(asv_table[,1] ~ timepoint + Error(Host.Name/timepoint), data = asv_table))
p_val <-tmp$`Error: Host.Name:timepoint`[[1]]$`Pr(>F)`[1]
anova_asv_res <- tibble(colnames(asv_table[1]), p_val)
colnames(anova_asv_res) <- c("ASV", "p_val")
#Run for each asv
for (i in 2:(length(colnames(asv_table))-2)) {
form <-as.formula(paste0("asv_table[," , i, "]", " ~ timepoint + Error(Host.Name/timepoint)"))
tmp <-summary(aov(formula = form, data = asv_table))
p_val <-tmp$`Error: Host.Name:timepoint`[[1]]$`Pr(>F)`[1]
tmpres <- tibble(colnames(asv_table[i]), p_val)
colnames(tmpres) <- c("ASV", "p_val")
anova_asv_res<- rbind(anova_asv_res, tmpres)
}
#find sig ones btwn timepoints
asv_sig_btwn_timep_CSS<-anova_asv_res$ASV[which(anova_asv_res$p_val <= 0.05)]
asv_sig_btwn_timep_CSS## [1] "GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG"
## [2] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGAAGGCTAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGGTCATCTAGAGTGTCGGAGGGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [3] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGATGGCTAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGGACTGCATTGGAAACTGGTTATCTTGAGTGTCGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGACAACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [4] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTTTGGCAAGTCTGATGTGAAAATCCGGGGCTCAACCCCGGAACTGCATTGGAAACTGTCAGACTAGAGTGTCGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [5] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGAAGAGCAAGTCTGATGTGAAAGGCTGGGGCTTAACCCCAGGACTGCATTGGAAACTGTTGTTCTAGAGTGCCGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [6] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGATGCAAGTCTGGAGTGAAAGCCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTATGGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [7] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGCGGTTTGACAAGTCAGAAGTGAAAGCCCGTGGCTCAACTGCGGGACTGCTTTTGAAACTGTGAGACTGGATTGCAGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACTGTAAATGACGCTGAGGCTCGAAAGCGTGGGG"
## [8] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGCAGTGCAAGTCAGATGTGAAAGGCCGGGGCTCAACCCCGGAGCTGCATTTGAAACTGCTCGGCTAGAGTACAGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACTGTTACTGACACTGAGGCACGAAAGCGTGGGG"
## [9] "GCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCTCGGCTTAACCGAGGAAGCGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGT"
## [10] "GCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCTCGGCTTAACCGAGGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGT"
## [11] "GCAAGCGTTGTCCGGAATCATTGGGCGTAAAGAGTTCGTAGGCGGCATGTCAAGTCTGGTGTTAAATCCTGAGGCTCAACTTCAGTTCAGCACTGGATACTGGCAAGCTAGAATGCGGTAGAGGTAAAGGGAATTCCTGGTGTAGCGGTGAAATGCGTAGATATCAGGAAGAACACCGGTGGCGTAAGCGCTTTACTGGGCCGTTATTGACGCTGAGGAACGAAAGCCGGGGG"
## [12] "GCAAGCGTTGTCCGGAATTACTGGGCGTAAAGGGTGCGTAGGCGGCCGTTTAAGTCAGATGTGAAATACCCGTGCTTAACATGGGGGCTGCATTTGAAACTGGATGGCTTGAGTGCAGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTGCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG"
## [13] "GCAAGCGTTGTCCGGAATTACTGGGCGTAAAGGGTGCGTAGGCGGCGCGTTAAGTCAGATGTGAAATACCCGTGCTTAACATGGGGGGTGCATCTGATACTGGCGTGCTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG"
## [14] "GCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAGCAAGTTGGAAGTGAAATCTGTGGGCTCAACTCACAAATTGCTTTCAAAACTGTTTTTCTTGAGTGGTGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACTAACTGACGCTGAGGCTCGAAAGCATGGGT"
## [15] "GCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGATCGTAAGTTGGGAGTGAAATTCATGGGCTCAACCCATGACCTGCTTTCAAAACTGCGATTCTTGAGTGGTGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACTAACTGACGCTGAGGCTCGAAAGCATGGGT"
## [16] "GCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGTGATCAAGTCAGCTGTGAAAACTACGGGCTTAACCCGTAGACTGCAGTTGAAACTGTTCATCTTGAGTGAAGTAGAGGTTGGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCGGTGGCGAAGGCGGCCAACTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG"
## [17] "GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGTGCGTAGGCGGCCGATCAAGTCAGGTGTGAAAGACCCGTGCTTAACATGGGGGTTGCACTTGAAACTGGTTGGCTTGAGTATGGGAGAGGCAAGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACCAAAACTGACGCTGAGGCACGAAAGCGTGGGT"
## [18] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGAGAAGCAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTGTTTCCCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG"
## [19] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGAGATGCAAGTCAGATGTGAAATCCCCGGGCTTAACCCGGGAACTGCATTTGAAACTGTATCCCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG"
## [20] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGAGATGCAAGTCAGATGTGAAATCCTCGGGCTTAACCCGGGAACTGCATTTGAAACTGTATCCCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG"
## [21] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAAACTGCATTTGAAACTGTAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT"
## [22] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGTTCGGCAAGTCAGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTTGAAACTGTCGAACTTGAGTGAAGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGT"
## [23] "GCGAGCGTTATCCGGAATTATTGGGTGTAAAGCGTGTGTAGGCGGGAAATTAAGTCTAAGGTCTAAGCCCGGAGCTCAACTCCGGTTCGCCTTAGAAACTGATTTTCTTGAGTGTGGTAGAGGCAAACGGAATTTCTAGTGTAGCGGTAAAATGCGTAGATATTAGAAGGAACACCAGTGGCGAAGGCGGTTTGCTGGGCCACTACTGACGCTGAGACACGAAAGCGTGGGG"
## [24] "t__262755"
## [25] "t__520"
## [26] "t__81974"#7 in common
asv_sig_btwn_timep_DES[asv_sig_btwn_timep_DES %in% asv_sig_btwn_timep_CSS]## [1] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGATGCAAGTCTGGAGTGAAAGCCCAGGGCTCAACCCTGGGACTGCTTTGGAAACTGTATGGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [2] "GCAAGCGTTGTCCGGAATTACTGGGCGTAAAGGGTGCGTAGGCGGCCGTTTAAGTCAGATGTGAAATACCCGTGCTTAACATGGGGGCTGCATTTGAAACTGGATGGCTTGAGTGCAGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTGCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG"
## [3] "GCAAGCGTTGTCCGGAATTACTGGGCGTAAAGGGTGCGTAGGCGGCGCGTTAAGTCAGATGTGAAATACCCGTGCTTAACATGGGGGGTGCATCTGATACTGGCGTGCTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG"
## [4] "GCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAGCAAGTTGGAAGTGAAATCTGTGGGCTCAACTCACAAATTGCTTTCAAAACTGTTTTTCTTGAGTGGTGTAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACTAACTGACGCTGAGGCTCGAAAGCATGGGT"
## [5] "GCAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGTGATCAAGTCAGCTGTGAAAACTACGGGCTTAACCCGTAGACTGCAGTTGAAACTGTTCATCTTGAGTGAAGTAGAGGTTGGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCGGTGGCGAAGGCGGCCAACTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG"
## [6] "t__262755"
## [7] "t__520"#remove these taxa from phyloseqs (since they are noise essentially)
filtered_ps003 <-prune_taxa(filtered_ps003, taxa = taxa_names(filtered_ps003)[!(taxa_names(filtered_ps003) %in% asv_sig_btwn_timep_DES)])
ps_CSS_norm_pass_min_postDD_sup003<- prune_taxa(ps_CSS_norm_pass_min_postDD_sup003, taxa = taxa_names(ps_CSS_norm_pass_min_postDD_sup003)[!(taxa_names(ps_CSS_norm_pass_min_postDD_sup003) %in% asv_sig_btwn_timep_CSS)])
ps_DeSeq_norm_pass_min_postDD_sup003<- prune_taxa(ps_DeSeq_norm_pass_min_postDD_sup003, taxa = taxa_names(ps_DeSeq_norm_pass_min_postDD_sup003)[!(taxa_names(ps_DeSeq_norm_pass_min_postDD_sup003) %in% asv_sig_btwn_timep_DES)])4 DA taxa identification
Identify bacterial and archaeal taxa (genera, species and strains) whose abundance is observed significantly more or less in the ASD
4.1 DESeq
dir.create(paste0(output_data, 'DESeq/'))
###Run DESeq proper (not the normalization but all of it)
runDESeq <- function(ps, dcut){
diagdds = phyloseq_to_deseq2(ps, ~ phenotype)
diagdds <- estimateSizeFactors(diagdds, type = "poscounts")
diagdds <- DESeq(diagdds,fitType="parametric", betaPrior = FALSE)
res = results(diagdds, contrast = c("phenotype", "N", "A"))
res$padj[is.na(res$padj)] = 1
sig <- res[res$padj < dcut,]
if (dim(sig)[1] == 0)
{sigtab<- as.data.frame(1, row.names="nothing")
colnames(sigtab) <- 'padj'}
else
{
sigtab <- data.frame(sig)
}
return(list(res, sigtab))
}
###Running analysis
###split thedata based on the real 3 timepoints
P1<-prune_samples(rownames(sample_data(filtered_ps003))[sample_data(filtered_ps003)$Within.study.sampling.date == "Timepoint 1"], filtered_ps003)
P2<-prune_samples(rownames(sample_data(filtered_ps003))[sample_data(filtered_ps003)$Within.study.sampling.date == "Timepoint 2"], filtered_ps003)
P3<-prune_samples(rownames(sample_data(filtered_ps003))[sample_data(filtered_ps003)$Within.study.sampling.date == "Timepoint 3"], filtered_ps003)
#several significants
deseq_res_P1 <- runDESeq(P1, deseq_cut)
deseq_res_P2 <- runDESeq(P2, deseq_cut)
deseq_res_P3 <- runDESeq(P3, deseq_cut)
# print significant taxa
datatable(deseq_res_P1[[2]])datatable(deseq_res_P2[[2]])datatable(deseq_res_P3[[2]])#"ASV_1669" present twice timepoint 1 and 3
# save
saveRDS(deseq_res_P1, file=paste0(output_data, "DESeq/deseq_res_P1_adjp", deseq_cut, ".Rda"))
saveRDS(deseq_res_P2, file=paste0(output_data, "DESeq/deseq_res_P2_adjp", deseq_cut, ".Rda"))
saveRDS(deseq_res_P3, file=paste0(output_data, "DESeq/deseq_res_P3_adjp", deseq_cut, ".Rda"))
#Working with time series
#according to the DeSeq vignette: design including the time factor, and then test using the likelihood ratio test as described
#the following section, where the time factor is removed in the reduced formula
runDESeq_time <- function(ps, dcut){
diagdds = phyloseq_to_deseq2(ps, ~ phenotype + Within.study.sampling.date)
diagdds <- estimateSizeFactors(diagdds, type = "poscounts")
diagdds <- DESeq(diagdds,fitType="parametric", betaPrior = FALSE)
#resultsNames(diagdds): to determine the constrast
res = results(diagdds, contrast = c("phenotype", "A", "N"))
res$padj[is.na(res$padj)] = 1
sig <- res[res$padj < dcut,]
if (dim(sig)[1] == 0)
{sigtab<- as.data.frame(1, row.names="nothing")
colnames(sigtab) <- 'padj'}
else
{
sigtab <- data.frame(sig)
}
return(list(res, sigtab))
}
#and this time when factoring in the interaction for longitudinal study!
bla<-runDESeq_time(filtered_ps003, deseq_cut)
saveRDS(bla, file=paste0(output_data, "DESeq/Deseq_time_interaction_adjp", deseq_cut, ".Rda"))
datatable(bla[2][[1]])# significant ASVs
row.names(bla[2][[1]])## [1] "ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT"
## [2] "t__262551"des_res<-cbind(row.names(bla[2][[1]]), as(tax_table(filtered_ps003)[row.names(bla[2][[1]]), ], "matrix"))
des_res_esv <- des_res
rownames(des_res) <- NULL
des_res##
## [1,] "ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT"
## [2,] "t__262551"
## Domain Phylum Class Order
## [1,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia" "o__Oscillospirales"
## [2,] "d__Bacteria" "p__Firmicutes" "c__Bacilli" "o__Erysipelotrichales"
## Family Genus Species
## [1,] "f__Ruminococcaceae" "g__Faecalibacterium" "s__unclassified"
## [2,] "f__Erysipelotrichaceae" "g__Holdemanella" "s__Holdemanella__biformis"
## Strain
## [1,] "t__unclassified"
## [2,] "t__262551"4.2 metagenomeSeq
dir.create(paste0(output_data, 'metagenomseq/'))
###Run ZIG model fitting and prediction
run_metagenom_seq<-function(ps,maxit, mcut){
p_metag<-phyloseq_to_metagenomeSeq(ps)
#filtering at least 4 samples
p_metag= cumNorm(p_metag, p=0.75)
normFactor =normFactors(p_metag)
normFactor =log2(normFactor/median(normFactor) + 1)
#mod = model.matrix(~ASDorNeuroT +PairASD+ normFactor)
mod = model.matrix(~phenotype + normFactor, data = pData(p_metag))
settings =zigControl(maxit =maxit, verbose =FALSE)
#settings =zigControl(tol = 1e-5, maxit = 30, verbose = TRUE, pvalMethod = 'bootstrap')
fit =fitZig(obj = p_metag, mod = mod, useCSSoffset = FALSE, control = settings)
#Note: changed fit$taxa to fit@taxa in light of error (probably from newer metagenomeseq ver.)
res_fit<-MRtable(fit, number = length(fit@taxa))
res_fit_nonfiltered <- copy(res_fit)
res_fit<-res_fit[res_fit$adjPvalues<mcut,]
#finally remove the ones that are not with enough samples
#mean_sample<-mean(calculateEffectiveSamples(fit))
#res_fit<-res_fit[res_fit$`counts in group 0` & res_fit$`counts in group 1` > mean_sample,]
Min_effec_samp<-calculateEffectiveSamples(fit)
Min_effec_samp<-Min_effec_samp[ names(Min_effec_samp) %in% rownames(res_fit)] #####there is a bug here
#manually removing the ones with "NA"
res_fit<-res_fit[grep("NA",rownames(res_fit), inv=T),]
res_fit$Min_sample<-Min_effec_samp
res_fit<-res_fit[res_fit$`+samples in group 0` >= Min_effec_samp & res_fit$`+samples in group 1` >= Min_effec_samp,]
return(list(res_fit_nonfiltered, res_fit))
}
run_metagenom_seq2<-function(ps,maxit, mcut){
p_metag<-phyloseq_to_metagenomeSeq(ps)
#filtering at least 4 samples
p_metag= cumNorm(p_metag, p=0.75)
normFactor =normFactors(p_metag)
normFactor =log2(normFactor/median(normFactor) + 1)
#mod = model.matrix(~ASDorNeuroT +PairASD+ normFactor)
mod = model.matrix(~phenotype + Within.study.sampling.date +normFactor, data = pData(p_metag))
settings =zigControl(maxit =maxit, verbose =FALSE)
#settings =zigControl(tol = 1e-5, maxit = 30, verbose = TRUE, pvalMethod = 'bootstrap')
fit =fitZig(obj = p_metag, mod = mod, useCSSoffset = FALSE, control = settings)
#Note: changed fit$taxa to fit@taxa in light of error (probably from newer metagenomeseq ver.)
res_fit<-MRtable(fit, number = length(fit@taxa))
res_fit_nonfiltered <- copy(res_fit)
res_fit<-res_fit[res_fit$adjPvalues<mcut,]
#finally remove the ones that are not with enough samples
#mean_sample<-mean(calculateEffectiveSamples(fit))
#res_fit<-res_fit[res_fit$`counts in group 0` & res_fit$`counts in group 1` > mean_sample,]
Min_effec_samp<-calculateEffectiveSamples(fit)
Min_effec_samp<-Min_effec_samp[ names(Min_effec_samp) %in% rownames(res_fit)] #####there is a bug here
#manually removing the ones with "NA"
res_fit<-res_fit[grep("NA",rownames(res_fit), inv=T),]
res_fit$Min_sample<-Min_effec_samp
res_fit<-res_fit[res_fit$`+samples in group 0` >= Min_effec_samp & res_fit$`+samples in group 1` >= Min_effec_samp,]
return(list(res_fit_nonfiltered, res_fit))
}
#Now for each time
P1<-prune_samples(rownames(sample_data(filtered_ps003))[sample_data(filtered_ps003)$Within.study.sampling.date == "Timepoint 1"], filtered_ps003)
P2<-prune_samples(rownames(sample_data(filtered_ps003))[sample_data(filtered_ps003)$Within.study.sampling.date == "Timepoint 2"], filtered_ps003)
P3<-prune_samples(rownames(sample_data(filtered_ps003))[sample_data(filtered_ps003)$Within.study.sampling.date == "Timepoint 3"], filtered_ps003)
zig_res_P1 <- run_metagenom_seq(P1,30, mtgseq_cut) # 30The maximum number of iterations for the expectation-maximization algorithm
zig_res_P2 <- run_metagenom_seq(P2,30, mtgseq_cut)
zig_res_P3 <- run_metagenom_seq(P3,30, mtgseq_cut)
zig_res_all<- run_metagenom_seq2(filtered_ps003,30, mtgseq_cut)
# print significant taxa
datatable(zig_res_P1[[2]])datatable(zig_res_P2[[2]])datatable(zig_res_P3[[2]])datatable(zig_res_all[[2]])zig_res_P1_filtered <- data.frame(cbind(zig_res_P1[[2]], tax_table(P1)[rownames(zig_res_P1[[2]]),]))
zig_res_P1_filtered$enriched <- ifelse(zig_res_P1_filtered$phenotypeN < 0, "Aut", "Control")
zig_res_P2_filtered <- data.frame(cbind(zig_res_P2[[2]], tax_table(P2)[rownames(zig_res_P2[[2]]), ]))
zig_res_P2_filtered$enriched <- ifelse(zig_res_P2_filtered$phenotypeN < 0, "Aut", "Control")
zig_res_P3_filtered <- data.frame(cbind(zig_res_P3[[2]], tax_table(P3)[rownames(zig_res_P3[[2]]), ]))
zig_res_P3_filtered$enriched <- ifelse(zig_res_P3_filtered$phenotypeN < 0, "Aut", "Control")
zig_res_all_filtered <- data.frame(cbind(zig_res_all[[2]], tax_table(filtered_ps003)[rownames(zig_res_all[[2]]), ]))
zig_res_all_filtered$enriched <- ifelse(zig_res_all_filtered$phenotypeN < 0, "Aut", "Control")
saveRDS(zig_res_P1, file=paste0(output_data, "metagenomseq/zig_res_P1_adjp", mtgseq_cut, ".rds"))
saveRDS(zig_res_P2, file=paste0(output_data, "metagenomseq/zig_res_P2_adjp", mtgseq_cut, ".rds"))
saveRDS(zig_res_P3, file=paste0(output_data, "metagenomseq/zig_res_P3_adjp", mtgseq_cut, ".rds"))
#do we have any in ESV in common?
#Reduce(intersect, list(rownames(zig_res_P1_filtered),rownames(zig_res_P2_filtered),rownames(zig_res_P3_filtered)))
#rownames(zig_res_P1[[2]])[which(rownames(zig_res_P1[[2]]) %in% c(rownames(zig_res_P2[[2]]), rownames(zig_res_P3[[2]])))]
#rownames(zig_res_P2[[2]])[which(rownames(zig_res_P2[[2]]) %in% rownames(zig_res_P3[[2]]))]
metag_res<-cbind(rownames(zig_res_all[[2]]), as(tax_table(filtered_ps003)[rownames(zig_res_all[[2]]), ], "matrix"))
metag_res_esv <-metag_res
rownames(metag_res) <- NULL
metag_res##
## [1,] "GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGTGGTGATTTAAGTCAGCGGTGAAAGTTTGTGGCTCAACCATAAAATTGCCGTTGAAACTGGGTTACTTGAGTGTGTTTGAGGTAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAACCATAACTGACACTGAAGCACGAAAGCGTGGGG"
## [2,] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGATTGGCAAGTCAGTAGTGAAATCCATGGGCTTAACCCATGACGTGCTATTGAAACTGTTGATCTTGAGTGAAGTAGAGGTAAGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGAGATCGGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGGCTTTAACTGACGCTGAGGCACGAAAGCATGGGT"
## [3,] "GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGAGATGCAAGTCAGATGTGAAATCCTCGGGCTTAACCCGGGAACTGCATTTGAAACTGTATCCCTTGAGTATCGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG"
## [4,] "GCAAGCGTTATCCGGAATGACTGGGCGTAAAGGGTGCGTAGGTGGTTTGTCAAGTTGGCAGCGTAATTCCGTGGCTTAACCGCGGAACTACTGCCAAAACTGATAGGCTTGAGTGCGGCAGGGGTATGTGGAATTCCTAGTGTAGCGGTGGAATGCGTAGATATTAGGAGGAACACCGGTGGCGAAAGCGACATACTGGGCCGTAACTGACACTGAAGCACGAAAGCGTGGGG"
## [5,] "t__186843"
## [6,] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAGGCAAGTCTGATGTGAAAACCCGGGGCTCAACCCCGTGACTGCATTGGAAACTGTTTTGCTTGAGTGCCGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGCAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [7,] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGTGTAGGTGGTATCACAAGTCAGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTTGAAACTGTGGAACTGGAGTGCAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACTGTAACTGACACTGAGGCTCGAAAGCGTGGGG"
## [8,] "GCGAGCGTTATCCGGATTCATTGGGCGTAAAGCGCGCGTAGGCGGCCCGGCAGGCCGGGGGTCGAAGCGGGGGGCTCAACCCCCCGAAGCCCCCGGAACCTCCGCGGCTTGGGTCCGGTAGGGGAGGGTGGAACACCCGGTGTAGCGGTGGAATGCGCAGATATCGGGTGGAACACCGGTGGCGAAGGCGGCCCTCTGGGCCGAGACCGACGCTGAGGCGCGAAAGCTGGGGG"
## [9,] "TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG"
## [10,] "GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATTGGTCAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTGCCAATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG"
## [11,] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGAATGGCAAGTCTGATGTGAAAGGCCGGGGCTCAACCCCGGGACTGCATTGGAAACTGTCAATCTAGAGTACCGGAGGGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [12,] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGACTGGCAAGTCTGATGTGAAAGGCGGGGGCTCAACCCCTGGACTGCATTGGAAACTGTTAGTCTTGAGTGCCGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [13,] "CCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTGATAAGTCTGAAGTTAAAGGCTGTGGCTCAACCATAGTTCGCTTTGGAAACTGTCAAACTTGAGTGCAGAAGGGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG"
## [14,] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGGAGTGCAAGTCAGATGTGAAAACTATGGGCTCAACCCATAGCCTGCATTTGAAACTGTACTTCTTGAGTGATGGAGAGGCAGGCGGAATTCCCTGTGTAGCGGTGAAATGCGTAGATATAGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACATTAACTGACGCTGAGGCGCGAAAGCGTGGGG"
## [15,] "CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGCAACTGACACTGATGCTCGAAAGTGTGGGT"
## [16,] "t__258798"
## [17,] "GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTATTAAGTTAGTGGTTAAATATTTGAGCTAAACTCAATTGTGCCATTAATACTGGTAAACTGGAGTACAGACGAGGTAGGCGGAATAAGTTAAGTAGCGGTGAAATGCATAGATATAACTTAGAACTCCGATAGCGAAGGCAGCTTACCAGACTGTAACTGACGCTGATGCACGAGAGCGTGGGT"
## [18,] "CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGACAGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCTGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGT"
## [19,] "GCGAGCGTTAATCGGAATAACTGGGCGTAAAGGGCACGCAGGCGGTGACTTAAGTGAGGTGTGAAAGCCCCGGGCTTAACCTGGGAATTGCATTTCATACTGGGTCGCTAGAGTACTTTAGGGAGGGGTAGAATTCCACGTGTAGCGGTGAAATGCGTAGAGATGTGGAGGAATACCGAAGGCGAAGGCAGCCCCTTGGGAATGTACTGACGCTCATGTGCGAAAGCGTGGGG"
## [20,] "GCGAGCGTTATCCGGAATTACTGGGTGTAAAGGGTGCGTAGGCGGCACCGTAAGTCTGTTGTGAAAGGCGATGGCTTAACCATCGAAGTGCAATGGAAACTGCGGAGCTAGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGCAACTGACGCTGAGGCACGAAAGCGTGGGG"
## [21,] "GCGAGCGTTATCCGGAATTATTGGGCGTAAAGAGCGCGCAGGTGGTTGATTAAGTCTGATGTGAAAGCCCACGGCTTAACCGTGGAGGGTCATTGGAAACTGGTCAACTTGAGTGCAGAAGAGGGAAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGAGATATGGAGGAACACCAGTGGCGAAGGCGGCTTCCTGGTCTGTAACTGACACTGAGGCGCGAAAGCGTGGGG"
## [22,] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTGTGGCAAGTCTGATGTGAAAGGCATGGGCTCAACCTGTGGACTGCATTGGAAACTGTCATACTTGAGTGCCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGG"
## [23,] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGGCGGTCTGACAAGTCAGAAGTGAAAGCCCGGGGCTCAACTCCGGGACTGCTTTTGAAACTGCCGGACTAGATTGCAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACTGTAAATGACGCTGAGGCTCGAAAGCGTGGGG"
## [24,] "t__220541"
## [25,] "t__209626"
## [26,] "GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGCAGTGCAAGTCAGATGTGAAAGGCCGGGGCTCAACCCCGGAGCTGCATTTGAAACTGCATAGCTAGAGTACAGGAGAGGCAGGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGACTGTTACTGACACTGAGGCACGAAAGCGTGGGG"
## [27,] "GCGAGCGTTGTCCGGAATTACTGGGCGTAAAGGGTGCGTAGGCGGTCTGAAAAGTCGGATGTGAAATCCCCGTGCTTAACATGGGAGCTGCATTCGAAACTTTCGGACTTGAGTGTCGGAGAGGTAAGCGGAATTCCCGGTGTAGCGGTGAAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGACAACTGACGCTGAGGCACGAAAGCGTGGGG"
## Domain Phylum Class
## [1,] "d__Bacteria" "p__Bacteroidota" "c__Bacteroidia"
## [2,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [3,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [4,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [5,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [6,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [7,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [8,] "d__Bacteria" "p__Actinobacteriota" "c__Coriobacteriia"
## [9,] "d__Bacteria" "p__Verrucomicrobiota" "c__Verrucomicrobiae"
## [10,] "d__Bacteria" "p__Firmicutes_C" "c__Negativicutes"
## [11,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [12,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [13,] "d__Bacteria" "p__Firmicutes" "c__Bacilli"
## [14,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [15,] "d__Bacteria" "p__Bacteroidota" "c__Bacteroidia"
## [16,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [17,] "d__Bacteria" "p__Bacteroidota" "c__Bacteroidia"
## [18,] "d__Bacteria" "p__Bacteroidota" "c__Bacteroidia"
## [19,] "d__Bacteria" "p__Proteobacteria" "c__Gammaproteobacteria"
## [20,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [21,] "d__Bacteria" "p__Firmicutes" "c__Bacilli"
## [22,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [23,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [24,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [25,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [26,] "d__Bacteria" "p__Firmicutes_A" "c__Clostridia"
## [27,] "d__Bacteria" "p__Firmicutes_A" "c__unclassified"
## Order Family
## [1,] "o__Bacteroidales" "f__Tannerellaceae"
## [2,] "o__Oscillospirales" "f__Acutalibacteraceae"
## [3,] "o__Oscillospirales" "f__Oscillospiraceae"
## [4,] "o__unclassified" "f__unclassified"
## [5,] "o__Christensenellales" "f__Christensenellaceae"
## [6,] "o__Lachnospirales" "f__Lachnospiraceae"
## [7,] "o__Lachnospirales" "f__Lachnospiraceae"
## [8,] "o__Coriobacteriales" "f__Coriobacteriaceae"
## [9,] "o__Verrucomicrobiales" "f__Akkermansiaceae"
## [10,] "o__Veillonellales" "f__Veillonellaceae"
## [11,] "o__Lachnospirales" "f__Lachnospiraceae"
## [12,] "o__Lachnospirales" "f__Lachnospiraceae"
## [13,] "o__Lactobacillales" "f__Streptococcaceae"
## [14,] "o__Oscillospirales" "f__Oscillospiraceae"
## [15,] "o__Bacteroidales" "f__Bacteroidaceae"
## [16,] "o__Oscillospirales" "f__Acutalibacteraceae"
## [17,] "o__Bacteroidales" "f__Marinifilaceae"
## [18,] "o__Bacteroidales" "f__Bacteroidaceae"
## [19,] "o__Enterobacterales" "f__Pasteurellaceae"
## [20,] "o__unclassified" "f__unclassified"
## [21,] "o__Haloplasmatales" "f__Turicibacteraceae"
## [22,] "o__Lachnospirales" "f__Lachnospiraceae"
## [23,] "o__Lachnospirales" "f__Lachnospiraceae"
## [24,] "o__Lachnospirales" "f__Lachnospiraceae"
## [25,] "o__Lachnospirales" "f__Lachnospiraceae"
## [26,] "o__Lachnospirales" "f__Lachnospiraceae"
## [27,] "o__unclassified" "f__unclassified"
## Genus Species
## [1,] "g__Parabacteroides" "s__Parabacteroides__merdae"
## [2,] "g__unclassified" "s__unclassified"
## [3,] "g__unclassified" "s__unclassified"
## [4,] "g__unclassified" "s__unclassified"
## [5,] "g__PROV_t__186843" "s__PROV_t__186843"
## [6,] "g__unclassified" "s__unclassified"
## [7,] "g__unclassified" "s__unclassified"
## [8,] "g__Collinsella" "s__unclassified"
## [9,] "g__Akkermansia" "s__Akkermansia__muciniphila"
## [10,] "g__Veillonella" "s__unclassified"
## [11,] "g__Ruminococcus_A" "s__unclassified"
## [12,] "g__Blautia_A" "s__unclassified"
## [13,] "g__Streptococcus" "s__unclassified"
## [14,] "g__Intestinimonas" "s__Intestinimonas__butyriciproducens"
## [15,] "g__Bacteroides" "s__unclassified"
## [16,] "g__Anaeromassilibacillus" "s__PROV_t__258798"
## [17,] "g__Odoribacter" "s__Odoribacter__splanchnicus"
## [18,] "g__Bacteroides" "s__Bacteroides__thetaiotaomicron"
## [19,] "g__Haemophilus_D" "s__unclassified"
## [20,] "g__unclassified" "s__unclassified"
## [21,] "g__Turicibacter" "s__unclassified"
## [22,] "g__Blautia_A" "s__Blautia_A__wexlerae"
## [23,] "g__PROV_t__182732" "s__unclassified"
## [24,] "g__Blautia_A" "s__PROV_t__220541"
## [25,] "g__PROV_t__209626" "s__PROV_t__209626"
## [26,] "g__Eubacterium_E" "s__Eubacterium_E__hallii_A"
## [27,] "g__unclassified" "s__unclassified"
## Strain
## [1,] "t__unclassified"
## [2,] "t__unclassified"
## [3,] "t__unclassified"
## [4,] "t__unclassified"
## [5,] "t__186843"
## [6,] "t__unclassified"
## [7,] "t__unclassified"
## [8,] "t__unclassified"
## [9,] "t__unclassified"
## [10,] "t__unclassified"
## [11,] "t__unclassified"
## [12,] "t__unclassified"
## [13,] "t__unclassified"
## [14,] "t__unclassified"
## [15,] "t__unclassified"
## [16,] "t__258798"
## [17,] "t__unclassified"
## [18,] "t__unclassified"
## [19,] "t__unclassified"
## [20,] "t__unclassified"
## [21,] "t__unclassified"
## [22,] "t__unclassified"
## [23,] "t__unclassified"
## [24,] "t__220541"
## [25,] "t__209626"
## [26,] "t__unclassified"
## [27,] "t__unclassified"4.3 ANCOM
#ANCOM_old ver (2.1 can be seen after)
#retrive taxa
#format metadata
#metada_ps<-sample_data(filtered_ps003)
#metada_ps<-as.data.frame(metada_ps)
#res_table_filt<-otu_table(filtered_ps003)
#res_table_filt<-as.data.frame(res_table_filt)
#res_table_filt<-t(res_table_filt)
#res_table_filt<-as.data.frame(res_table_filt)
#res_table_filt$metada<-metada_ps$phenotype[rownames(metada_ps)%in%rownames(res_table_filt)]
#ancom.all.filt.0.05 <- ANCOM(res_table_filt, sig = 0.05, multcorr = 2)
#saveRDS(ancom.all.filt.0.05, file=paste0(output_data, "ANCOM/ancom_res", mtgseq_cut, ".rds"))
#Format the Detected Table w/ Taxa
#main.05 <- cbind(ancom.all.filt.0.05$detected, as(tax_table(filtered_ps003)[ancom.all.filt.0.05$detected, ], "matrix"))
#main.05esv <- main.05
#row.names(main.05) <- NULL
#main.05
#New ANCOM 2.1
#retrive taxa
#format metadata
metada_ps<-sample_data(filtered_ps003)
metada_ps<-as.data.frame(metada_ps)
metada_ps$HostName <- as.character(metada_ps$Host.Name)
metada_ps$phenotype <- as.character(metada_ps$phenotype)
metada_ps<-tibble(metada_ps$HostName, metada_ps$phenotype, rows = rownames(metada_ps))
colnames(metada_ps) <- c("HostName", "phenotype", "Biospecimen.Barcode")
res_table_filt<-otu_table(filtered_ps003)
res_table_filt<-as.data.frame(res_table_filt)
prepro<-feature_table_pre_process(feature_table = res_table_filt, meta_data = metada_ps, sample_var = "Biospecimen.Barcode", group_var = "phenotype", out_cut = 0.05, zero_cut = 0.90, lib_cut = 1000, neg_lb = FALSE)
feature_table = prepro$feature_table # Preprocessed feature table
meta_data = prepro$meta_data # Preprocessed metadata
struc_zero = prepro$structure_zeros # Structural zero info
main_var = "phenotype"; p_adj_method = "BH"; alpha = 0.05
adj_formula = NULL; rand_formula = "~ 1 | HostName" ; control = list(msMaxIter = 50)
ancom.all.filt.0.05 <- ANCOM(feature_table, meta_data, struc_zero, main_var, p_adj_method,
alpha, adj_formula, rand_formula)#, control)
dir.create(path = "ANCOM")
saveRDS(ancom.all.filt.0.05, file=paste0(output_data, "ANCOM/ancom_res", mtgseq_cut, ".rds"))
#Format the Detected Table w/ Taxa
ancom.all.filt.0.05$detected## NULL#no significant taxa from ANCOM
#Trying by each timepoint
#Timepoint 3
ps3<-subset_samples(filtered_ps003, Within.study.sampling.date == "Timepoint 3" )
metada_ps<-sample_data(ps3)
metada_ps<-as.data.frame(metada_ps)
metada_ps$HostName <- as.character(metada_ps$Host.Name)
metada_ps$phenotype <- as.character(metada_ps$phenotype)
metada_ps<-tibble(metada_ps$HostName, metada_ps$phenotype, rows = rownames(metada_ps))
colnames(metada_ps) <- c("HostName", "phenotype", "Biospecimen.Barcode")
res_table_filt<-otu_table(ps3)
res_table_filt<-as.data.frame(res_table_filt)
prepro<-feature_table_pre_process(feature_table = res_table_filt, meta_data = metada_ps, sample_var = "Biospecimen.Barcode", group_var = "phenotype", out_cut = 0.05, zero_cut = 0.90, lib_cut = 1000, neg_lb = FALSE)
feature_table = prepro$feature_table # Preprocessed feature table
meta_data = prepro$meta_data # Preprocessed metadata
struc_zero = prepro$structure_zeros # Structural zero info
main_var = "phenotype"; p_adj_method = "BH"; alpha = 0.05
adj_formula = NULL; rand_formula = NULL
ancom.all.filt.0.05.3 <- ANCOM(feature_table, meta_data, struc_zero, main_var, p_adj_method,
alpha, adj_formula, rand_formula)#, control)
#dir.create(path = "ANCOM")
saveRDS(ancom.all.filt.0.05.3, file=paste0(output_data, "ANCOM/ancom_res3_", mtgseq_cut, ".rds"))
#Format the Detected Table w/ Taxa
ancom.all.filt.0.05.3$detected## NULL# No sig
#Timepoint 2
ps2<-subset_samples(filtered_ps003, Within.study.sampling.date == "Timepoint 2" )
metada_ps<-sample_data(ps2)
metada_ps<-as.data.frame(metada_ps)
metada_ps$HostName <- as.character(metada_ps$Host.Name)
metada_ps$phenotype <- as.character(metada_ps$phenotype)
metada_ps<-tibble(metada_ps$HostName, metada_ps$phenotype, rows = rownames(metada_ps))
colnames(metada_ps) <- c("HostName", "phenotype", "Biospecimen.Barcode")
res_table_filt<-otu_table(ps2)
res_table_filt<-as.data.frame(res_table_filt)
prepro<-feature_table_pre_process(feature_table = res_table_filt, meta_data = metada_ps, sample_var = "Biospecimen.Barcode", group_var = "phenotype", out_cut = 0.05, zero_cut = 0.90, lib_cut = 1000, neg_lb = FALSE)
feature_table = prepro$feature_table # Preprocessed feature table
meta_data = prepro$meta_data # Preprocessed metadata
struc_zero = prepro$structure_zeros # Structural zero info
main_var = "phenotype"; p_adj_method = "BH"; alpha = 0.05
adj_formula = NULL; rand_formula = NULL
ancom.all.filt.0.05.2 <- ANCOM(feature_table, meta_data, struc_zero, main_var, p_adj_method,
alpha, adj_formula, rand_formula)#, control)
#dir.create(path = "ANCOM")
saveRDS(ancom.all.filt.0.05.2, file=paste0(output_data, "ANCOM/ancom_res2_", mtgseq_cut, ".rds"))
#Format the Detected Table w/ Taxa
ancom.all.filt.0.05.2$detected## NULL# No sig
#Timepoint 1
ps1<-subset_samples(filtered_ps003, Within.study.sampling.date == "Timepoint 1" )
metada_ps<-sample_data(ps1)
metada_ps<-as.data.frame(metada_ps)
metada_ps$HostName <- as.character(metada_ps$Host.Name)
metada_ps$phenotype <- as.character(metada_ps$phenotype)
metada_ps<-tibble(metada_ps$HostName, metada_ps$phenotype, rows = rownames(metada_ps))
colnames(metada_ps) <- c("HostName", "phenotype", "Biospecimen.Barcode")
res_table_filt<-otu_table(ps1)
res_table_filt<-as.data.frame(res_table_filt)
prepro<-feature_table_pre_process(feature_table = res_table_filt, meta_data = metada_ps, sample_var = "Biospecimen.Barcode", group_var = "phenotype", out_cut = 0.05, zero_cut = 0.90, lib_cut = 1000, neg_lb = FALSE)
feature_table = prepro$feature_table # Preprocessed feature table
meta_data = prepro$meta_data # Preprocessed metadata
struc_zero = prepro$structure_zeros # Structural zero info
main_var = "phenotype"; p_adj_method = "BH"; alpha = 0.05
adj_formula = NULL; rand_formula = NULL
ancom.all.filt.0.05.1 <- ANCOM(feature_table, meta_data, struc_zero, main_var, p_adj_method,
alpha, adj_formula, rand_formula)#, control)
#dir.create(path = "ANCOM")
saveRDS(ancom.all.filt.0.05.1, file=paste0(output_data, "ANCOM/ancom_res1_", mtgseq_cut, ".rds"))
#Format the Detected Table w/ Taxa
ancom.all.filt.0.05.1$detected## NULL# No sig4.4 Results table summarizing significant taxa
#Deseq results
#Assemble Deseq results into one table
DesP1<-tibble(rownames(deseq_res_P1[[2]]), rep("DESeq2_P1", length(rownames(deseq_res_P1[[2]]))))
colnames(DesP1) <- c("ASV", "Method+Data")
DesP2<-tibble(rownames(deseq_res_P2[[2]]), rep("DESeq2_P2", length(rownames(deseq_res_P2[[2]]))))
colnames(DesP2) <- c("ASV", "Method+Data")
DesP3<-tibble(rownames(deseq_res_P3[[2]]), rep("DESeq2_P3", length(rownames(deseq_res_P3[[2]]))))
colnames(DesP3) <- c("ASV", "Method+Data")
DesAll<-tibble(des_res[,1], rep("DESeq2_All", length(des_res[,1])))
colnames(DesAll) <- c("ASV", "Method+Data")
deseq_allsig<-rbind(DesP1,DesP2, DesP3, DesAll)
#Find ones found multiple times (either in indiviudual timepoints or all together) and add them back in with correct labeling
deseqcount<-plyr::ddply(deseq_allsig, "ASV", transform, count = length(ASV))
duplic<-tibble(deseqcount$ASV[which(deseqcount$count == 2)], deseqcount$Method.Data[which(deseqcount$count == 2)])
a<-tibble(duplic$`deseqcount$ASV[which(deseqcount$count == 2)]`[1], "DESeq2_P3+All")
colnames(a) <- c("ASV", "Method+Data")
b<-tibble(duplic$`deseqcount$ASV[which(deseqcount$count == 2)]`[3], "DESeq2_P1+P3")
colnames(b) <- c("ASV", "Method+Data")
to_add_backin<-rbind(a,b)
deseq_allsig<-deseq_allsig[-which(deseq_allsig$ASV == deseqcount$ASV[which(deseqcount$count == 2)][1]),]
deseq_allsig<-deseq_allsig[-which(deseq_allsig$ASV == deseqcount$ASV[which(deseqcount$count == 2)][3]),]
deseq_allsig<-rbind(deseq_allsig, to_add_backin)
deseq_allsig<-cbind(deseq_allsig, as(tax_table(filtered_ps003)[deseq_allsig$ASV, ], "matrix"))
saveRDS(deseq_allsig, "DESeq/full_res_table_deseq")
deseq_allsig.print <- deseq_allsig
deseq_allsig.print$ASV <- NULL
rownames(deseq_allsig.print) <- NULL
deseq_allsig.print## Method+Data Domain Phylum Class
## 1 DESeq2_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 2 DESeq2_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 3 DESeq2_P1 d__Bacteria p__Firmicutes_C c__Negativicutes
## 4 DESeq2_P2 d__Bacteria p__unclassified c__unclassified
## 5 DESeq2_P2 d__Archaea p__Euryarchaeota c__Methanobacteria
## 6 DESeq2_P3 d__Bacteria p__unclassified c__unclassified
## 7 DESeq2_P3 d__Bacteria p__Bacteroidota c__Bacteroidia
## 8 DESeq2_P3 d__Bacteria p__Firmicutes c__Bacilli
## 9 DESeq2_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 10 DESeq2_All d__Bacteria p__Firmicutes c__Bacilli
## 11 DESeq2_P3+All d__Bacteria p__Firmicutes_A c__Clostridia
## 12 DESeq2_P1+P3 d__Bacteria p__Bacteroidota c__Bacteroidia
## Order Family Genus
## 1 o__Oscillospirales f__Ruminococcaceae g__Faecalibacterium
## 2 o__unclassified f__unclassified g__unclassified
## 3 o__Veillonellales f__Dialisteraceae g__Dialister
## 4 o__unclassified f__unclassified g__unclassified
## 5 o__Methanobacteriales f__Methanobacteriaceae g__Methanobrevibacter_A
## 6 o__unclassified f__unclassified g__unclassified
## 7 o__Bacteroidales f__Bacteroidaceae g__Bacteroides
## 8 o__Lactobacillales f__Streptococcaceae g__Lactococcus
## 9 o__Lachnospirales f__Lachnospiraceae g__Blautia_A
## 10 o__Erysipelotrichales f__Erysipelotrichaceae g__Holdemanella
## 11 o__Oscillospirales f__Ruminococcaceae g__Faecalibacterium
## 12 o__Bacteroidales f__Barnesiellaceae g__Barnesiella
## Species Strain
## 1 s__unclassified t__unclassified
## 2 s__unclassified t__unclassified
## 3 s__PROV_t__4989 t__4989
## 4 s__unclassified t__unclassified
## 5 s__unclassified t__unclassified
## 6 s__unclassified t__unclassified
## 7 s__Bacteroides__intestinalis t__unclassified
## 8 s__unclassified t__unclassified
## 9 s__unclassified t__unclassified
## 10 s__Holdemanella__biformis t__262551
## 11 s__unclassified t__unclassified
## 12 s__Barnesiella__intestinihominis t__21316#MTG results
MtgP1<-tibble(rownames(zig_res_P1[[2]]), rep("Mtg_P1", length(rownames(zig_res_P1[[2]]))))
colnames(MtgP1) <- c("ASV", "Method+Data")
MtgP2<-tibble(rownames(zig_res_P2[[2]]), rep("Mtg_P2", length(rownames(zig_res_P2[[2]]))))
colnames(MtgP2) <- c("ASV", "Method+Data")
MtgP3<-tibble(rownames(zig_res_P3[[2]]), rep("Mtg_P3", length(rownames(zig_res_P3[[2]]))))
colnames(MtgP3) <- c("ASV", "Method+Data")
MtgAll<-tibble(metag_res[,1], rep("Mtg_All", length(metag_res[,1])))
colnames(MtgAll) <- c("ASV", "Method+Data")
Mtg_allsig<-rbind(MtgP1,MtgP2, MtgP3, MtgAll)
Mtgcount<-plyr::ddply(Mtg_allsig, "ASV", transform, count = length(ASV))
duplic<-tibble(Mtgcount$ASV[which(Mtgcount$count >= 2)], Mtgcount$Method.Data[which(Mtgcount$count >= 2)])
replace <-paste(duplic$`Mtgcount$Method.Data[which(Mtgcount$count >= 2)]`[which(duplic$`Mtgcount$ASV[which(Mtgcount$count >= 2)]` == unique(duplic$`Mtgcount$ASV[which(Mtgcount$count >= 2)]`)[1])], collapse = "")
tmp<-tibble(unique(duplic$`Mtgcount$ASV[which(Mtgcount$count >= 2)]`)[1], replace)
colnames(tmp) <- c("ASV", "Method+Data")
replacetab <-tmp
for (i in 2:length(unique(duplic$`Mtgcount$ASV[which(Mtgcount$count >= 2)]`))) {
replace <-paste(duplic$`Mtgcount$Method.Data[which(Mtgcount$count >= 2)]`[which(duplic$`Mtgcount$ASV[which(Mtgcount$count >= 2)]` == unique(duplic$`Mtgcount$ASV[which(Mtgcount$count >= 2)]`)[i])], collapse = "")
tmp<-tibble(unique(duplic$`Mtgcount$ASV[which(Mtgcount$count >= 2)]`)[i], replace)
colnames(tmp) <- c("ASV", "Method+Data")
replacetab<-rbind(replacetab, tmp)
}
Mtg_allsig<-Mtg_allsig[-which(Mtg_allsig$ASV %in% replacetab$ASV),]
Mtg_allsig<-rbind(Mtg_allsig, replacetab)
Mtg_allsig<-cbind(Mtg_allsig, as(tax_table(filtered_ps003)[Mtg_allsig$ASV, ], "matrix"))
saveRDS(Mtg_allsig, "metagenomseq//full_res_table_mtg")
Mtg_allsig.print <- Mtg_allsig
Mtg_allsig.print$ASV <- NULL
rownames(Mtg_allsig.print) <- NULL
Mtg_allsig.print## Method+Data Domain Phylum Class
## 1 Mtg_P1 d__Bacteria p__Actinobacteriota c__Actinobacteria
## 2 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 3 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 4 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 5 Mtg_P1 d__Bacteria p__Firmicutes c__Bacilli
## 6 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 7 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 8 Mtg_P1 d__Bacteria p__Firmicutes c__Bacilli
## 9 Mtg_P1 d__Bacteria p__Firmicutes c__Bacilli
## 10 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 11 Mtg_P2 d__Bacteria p__Firmicutes c__Bacilli
## 12 Mtg_P2 d__Bacteria p__Firmicutes_A c__Clostridia
## 13 Mtg_P2 d__Bacteria p__Firmicutes_A c__Clostridia
## 14 Mtg_P2 d__Bacteria p__Firmicutes_A c__Clostridia
## 15 Mtg_P2 d__Bacteria p__Firmicutes c__Bacilli
## 16 Mtg_P2 d__Bacteria p__Bacteroidota c__Bacteroidia
## 17 Mtg_P2 d__Bacteria p__unclassified c__unclassified
## 18 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 19 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 20 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 21 Mtg_P3 d__Bacteria p__Firmicutes c__Bacilli
## 22 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 23 Mtg_P3 d__Bacteria p__Bacteroidota c__Bacteroidia
## 24 Mtg_P3 d__Bacteria p__Firmicutes c__Bacilli
## 25 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 26 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 27 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 28 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 29 Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 30 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 31 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 32 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 33 Mtg_All d__Bacteria p__Verrucomicrobiota c__Verrucomicrobiae
## 34 Mtg_All d__Bacteria p__Firmicutes c__Bacilli
## 35 Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 36 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 37 Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 38 Mtg_All d__Bacteria p__Proteobacteria c__Gammaproteobacteria
## 39 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 40 Mtg_All d__Bacteria p__Firmicutes c__Bacilli
## 41 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 42 Mtg_All d__Bacteria p__Firmicutes_A c__unclassified
## 43 Mtg_P2Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 44 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 45 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 46 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 47 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 48 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 49 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 50 Mtg_P2Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 51 Mtg_P1Mtg_P3 d__Bacteria p__Firmicutes_C c__Negativicutes
## 52 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_C c__Negativicutes
## 53 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 54 Mtg_P1Mtg_P2Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 55 Mtg_P2Mtg_All d__Bacteria p__Actinobacteriota c__Coriobacteriia
## 56 Mtg_P3Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 57 Mtg_P2Mtg_P3Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 58 Mtg_P2Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## Order Family
## 1 o__Actinomycetales f__Bifidobacteriaceae
## 2 o__Lachnospirales f__Lachnospiraceae
## 3 o__Lachnospirales f__Lachnospiraceae
## 4 o__Lachnospirales f__Lachnospiraceae
## 5 o__Lactobacillales f__Streptococcaceae
## 6 o__Oscillospirales f__Oscillospiraceae
## 7 o__Lachnospirales f__Lachnospiraceae
## 8 o__Erysipelotrichales f__Erysipelotrichaceae
## 9 o__Staphylococcales f__Gemellaceae
## 10 o__Eubacteriales f__Anaerofustaceae
## 11 o__Erysipelotrichales f__Erysipelotrichaceae
## 12 o__Lachnospirales f__Lachnospiraceae
## 13 o__Oscillospirales f__Acutalibacteraceae
## 14 o__Lachnospirales f__Lachnospiraceae
## 15 o__Erysipelotrichales f__Erysipelotrichaceae
## 16 o__Bacteroidales f__Bacteroidaceae
## 17 o__unclassified f__unclassified
## 18 o__Oscillospirales f__Ruminococcaceae
## 19 o__Lachnospirales f__Lachnospiraceae
## 20 o__Lachnospirales f__Lachnospiraceae
## 21 o__Lactobacillales f__Streptococcaceae
## 22 o__Lachnospirales f__Lachnospiraceae
## 23 o__Bacteroidales f__Bacteroidaceae
## 24 o__Erysipelotrichales f__Erysipelatoclostridiaceae
## 25 o__Lachnospirales f__Anaerotignaceae
## 26 o__Lachnospirales f__Lachnospiraceae
## 27 o__Lachnospirales f__Lachnospiraceae
## 28 o__Oscillospirales f__Ruminococcaceae
## 29 o__Bacteroidales f__Tannerellaceae
## 30 o__Oscillospirales f__Acutalibacteraceae
## 31 o__unclassified f__unclassified
## 32 o__Lachnospirales f__Lachnospiraceae
## 33 o__Verrucomicrobiales f__Akkermansiaceae
## 34 o__Lactobacillales f__Streptococcaceae
## 35 o__Bacteroidales f__Bacteroidaceae
## 36 o__Oscillospirales f__Acutalibacteraceae
## 37 o__Bacteroidales f__Bacteroidaceae
## 38 o__Enterobacterales f__Pasteurellaceae
## 39 o__unclassified f__unclassified
## 40 o__Haloplasmatales f__Turicibacteraceae
## 41 o__Lachnospirales f__Lachnospiraceae
## 42 o__unclassified f__unclassified
## 43 o__Eubacteriales f__Eubacteriaceae
## 44 o__Lachnospirales f__Lachnospiraceae
## 45 o__Lachnospirales f__Lachnospiraceae
## 46 o__Lachnospirales f__Lachnospiraceae
## 47 o__Lachnospirales f__Lachnospiraceae
## 48 o__Lachnospirales f__Lachnospiraceae
## 49 o__Oscillospirales f__Oscillospiraceae
## 50 o__Lachnospirales f__Lachnospiraceae
## 51 o__Veillonellales f__Veillonellaceae
## 52 o__Veillonellales f__Veillonellaceae
## 53 o__Oscillospirales f__Oscillospiraceae
## 54 o__Peptostreptococcales f__Anaerovoracaceae
## 55 o__Coriobacteriales f__Coriobacteriaceae
## 56 o__Bacteroidales f__Marinifilaceae
## 57 o__Christensenellales f__Christensenellaceae
## 58 o__Lachnospirales f__Lachnospiraceae
## Genus Species
## 1 g__Bifidobacterium s__Bifidobacterium__bifidum
## 2 g__unclassified s__unclassified
## 3 g__Anaerostipes s__Anaerostipes__hadrus
## 4 g__Blautia_A s__unclassified
## 5 g__Streptococcus s__unclassified
## 6 g__Lawsonibacter s__unclassified
## 7 g__Clostridium_M s__Clostridium_M__asparagiforme
## 8 g__Solobacterium s__Solobacterium__moorei
## 9 g__Gemella s__unclassified
## 10 g__Anaerofustis s__Anaerofustis__stercorihominis
## 11 g__Absiella s__unclassified
## 12 g__Blautia_A s__PROV_t__165457
## 13 g__Acutalibacter s__Acutalibacter__timonensis
## 14 g__Blautia s__unclassified
## 15 g__Holdemania s__unclassified
## 16 g__Bacteroides s__unclassified
## 17 g__unclassified s__unclassified
## 18 g__Faecalibacterium s__Faecalibacterium__prausnitzii_K
## 19 g__Coprococcus_A s__Coprococcus_A__catus
## 20 g__unclassified s__unclassified
## 21 g__Lactococcus s__unclassified
## 22 g__unclassified s__unclassified
## 23 g__Bacteroides_B s__Bacteroides_B__vulgatus
## 24 g__Erysipelatoclostridium s__unclassified
## 25 g__Anaerotignum s__unclassified
## 26 g__Eubacterium_E s__unclassified
## 27 g__PROV_t__256727 s__PROV_t__256727
## 28 g__unclassified s__unclassified
## 29 g__Parabacteroides s__Parabacteroides__merdae
## 30 g__unclassified s__unclassified
## 31 g__unclassified s__unclassified
## 32 g__unclassified s__unclassified
## 33 g__Akkermansia s__Akkermansia__muciniphila
## 34 g__Streptococcus s__unclassified
## 35 g__Bacteroides s__unclassified
## 36 g__Anaeromassilibacillus s__PROV_t__258798
## 37 g__Bacteroides s__Bacteroides__thetaiotaomicron
## 38 g__Haemophilus_D s__unclassified
## 39 g__unclassified s__unclassified
## 40 g__Turicibacter s__unclassified
## 41 g__Blautia_A s__PROV_t__220541
## 42 g__unclassified s__unclassified
## 43 g__Eubacterium s__unclassified
## 44 g__Ruminococcus_A s__unclassified
## 45 g__Blautia_A s__unclassified
## 46 g__Blautia_A s__Blautia_A__wexlerae
## 47 g__PROV_t__182732 s__unclassified
## 48 g__unclassified s__unclassified
## 49 g__Intestinimonas s__Intestinimonas__butyriciproducens
## 50 g__Eubacterium_E s__Eubacterium_E__hallii_A
## 51 g__Veillonella s__unclassified
## 52 g__Veillonella s__unclassified
## 53 g__unclassified s__unclassified
## 54 g__unclassified s__unclassified
## 55 g__Collinsella s__unclassified
## 56 g__Odoribacter s__Odoribacter__splanchnicus
## 57 g__PROV_t__186843 s__PROV_t__186843
## 58 g__PROV_t__209626 s__PROV_t__209626
## Strain
## 1 t__unclassified
## 2 t__unclassified
## 3 t__158681
## 4 t__unclassified
## 5 t__unclassified
## 6 t__unclassified
## 7 t__unclassified
## 8 t__unclassified
## 9 t__unclassified
## 10 t__unclassified
## 11 t__unclassified
## 12 t__165457
## 13 t__145645
## 14 t__unclassified
## 15 t__unclassified
## 16 t__unclassified
## 17 t__unclassified
## 18 t__unclassified
## 19 t__92557
## 20 t__unclassified
## 21 t__unclassified
## 22 t__unclassified
## 23 t__21615
## 24 t__unclassified
## 25 t__unclassified
## 26 t__unclassified
## 27 t__256727
## 28 t__unclassified
## 29 t__unclassified
## 30 t__unclassified
## 31 t__unclassified
## 32 t__unclassified
## 33 t__unclassified
## 34 t__unclassified
## 35 t__unclassified
## 36 t__258798
## 37 t__unclassified
## 38 t__unclassified
## 39 t__unclassified
## 40 t__unclassified
## 41 t__220541
## 42 t__unclassified
## 43 t__unclassified
## 44 t__unclassified
## 45 t__unclassified
## 46 t__unclassified
## 47 t__unclassified
## 48 t__unclassified
## 49 t__unclassified
## 50 t__unclassified
## 51 t__unclassified
## 52 t__unclassified
## 53 t__unclassified
## 54 t__unclassified
## 55 t__unclassified
## 56 t__unclassified
## 57 t__186843
## 58 t__209626#None for ANCOM, so will concatenate mtg and des into one table
fullsigtab<-rbind(Mtg_allsig.print, deseq_allsig.print)
fullsigtab_esv<-rbind(Mtg_allsig, deseq_allsig)
fullsigtab## Method+Data Domain Phylum Class
## 1 Mtg_P1 d__Bacteria p__Actinobacteriota c__Actinobacteria
## 2 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 3 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 4 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 5 Mtg_P1 d__Bacteria p__Firmicutes c__Bacilli
## 6 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 7 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 8 Mtg_P1 d__Bacteria p__Firmicutes c__Bacilli
## 9 Mtg_P1 d__Bacteria p__Firmicutes c__Bacilli
## 10 Mtg_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 11 Mtg_P2 d__Bacteria p__Firmicutes c__Bacilli
## 12 Mtg_P2 d__Bacteria p__Firmicutes_A c__Clostridia
## 13 Mtg_P2 d__Bacteria p__Firmicutes_A c__Clostridia
## 14 Mtg_P2 d__Bacteria p__Firmicutes_A c__Clostridia
## 15 Mtg_P2 d__Bacteria p__Firmicutes c__Bacilli
## 16 Mtg_P2 d__Bacteria p__Bacteroidota c__Bacteroidia
## 17 Mtg_P2 d__Bacteria p__unclassified c__unclassified
## 18 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 19 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 20 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 21 Mtg_P3 d__Bacteria p__Firmicutes c__Bacilli
## 22 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 23 Mtg_P3 d__Bacteria p__Bacteroidota c__Bacteroidia
## 24 Mtg_P3 d__Bacteria p__Firmicutes c__Bacilli
## 25 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 26 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 27 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 28 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 29 Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 30 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 31 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 32 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 33 Mtg_All d__Bacteria p__Verrucomicrobiota c__Verrucomicrobiae
## 34 Mtg_All d__Bacteria p__Firmicutes c__Bacilli
## 35 Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 36 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 37 Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 38 Mtg_All d__Bacteria p__Proteobacteria c__Gammaproteobacteria
## 39 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 40 Mtg_All d__Bacteria p__Firmicutes c__Bacilli
## 41 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 42 Mtg_All d__Bacteria p__Firmicutes_A c__unclassified
## 43 Mtg_P2Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 44 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 45 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 46 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 47 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 48 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 49 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 50 Mtg_P2Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 51 Mtg_P1Mtg_P3 d__Bacteria p__Firmicutes_C c__Negativicutes
## 52 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_C c__Negativicutes
## 53 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 54 Mtg_P1Mtg_P2Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 55 Mtg_P2Mtg_All d__Bacteria p__Actinobacteriota c__Coriobacteriia
## 56 Mtg_P3Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 57 Mtg_P2Mtg_P3Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 58 Mtg_P2Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 59 DESeq2_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 60 DESeq2_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 61 DESeq2_P1 d__Bacteria p__Firmicutes_C c__Negativicutes
## 62 DESeq2_P2 d__Bacteria p__unclassified c__unclassified
## 63 DESeq2_P2 d__Archaea p__Euryarchaeota c__Methanobacteria
## 64 DESeq2_P3 d__Bacteria p__unclassified c__unclassified
## 65 DESeq2_P3 d__Bacteria p__Bacteroidota c__Bacteroidia
## 66 DESeq2_P3 d__Bacteria p__Firmicutes c__Bacilli
## 67 DESeq2_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 68 DESeq2_All d__Bacteria p__Firmicutes c__Bacilli
## 69 DESeq2_P3+All d__Bacteria p__Firmicutes_A c__Clostridia
## 70 DESeq2_P1+P3 d__Bacteria p__Bacteroidota c__Bacteroidia
## Order Family
## 1 o__Actinomycetales f__Bifidobacteriaceae
## 2 o__Lachnospirales f__Lachnospiraceae
## 3 o__Lachnospirales f__Lachnospiraceae
## 4 o__Lachnospirales f__Lachnospiraceae
## 5 o__Lactobacillales f__Streptococcaceae
## 6 o__Oscillospirales f__Oscillospiraceae
## 7 o__Lachnospirales f__Lachnospiraceae
## 8 o__Erysipelotrichales f__Erysipelotrichaceae
## 9 o__Staphylococcales f__Gemellaceae
## 10 o__Eubacteriales f__Anaerofustaceae
## 11 o__Erysipelotrichales f__Erysipelotrichaceae
## 12 o__Lachnospirales f__Lachnospiraceae
## 13 o__Oscillospirales f__Acutalibacteraceae
## 14 o__Lachnospirales f__Lachnospiraceae
## 15 o__Erysipelotrichales f__Erysipelotrichaceae
## 16 o__Bacteroidales f__Bacteroidaceae
## 17 o__unclassified f__unclassified
## 18 o__Oscillospirales f__Ruminococcaceae
## 19 o__Lachnospirales f__Lachnospiraceae
## 20 o__Lachnospirales f__Lachnospiraceae
## 21 o__Lactobacillales f__Streptococcaceae
## 22 o__Lachnospirales f__Lachnospiraceae
## 23 o__Bacteroidales f__Bacteroidaceae
## 24 o__Erysipelotrichales f__Erysipelatoclostridiaceae
## 25 o__Lachnospirales f__Anaerotignaceae
## 26 o__Lachnospirales f__Lachnospiraceae
## 27 o__Lachnospirales f__Lachnospiraceae
## 28 o__Oscillospirales f__Ruminococcaceae
## 29 o__Bacteroidales f__Tannerellaceae
## 30 o__Oscillospirales f__Acutalibacteraceae
## 31 o__unclassified f__unclassified
## 32 o__Lachnospirales f__Lachnospiraceae
## 33 o__Verrucomicrobiales f__Akkermansiaceae
## 34 o__Lactobacillales f__Streptococcaceae
## 35 o__Bacteroidales f__Bacteroidaceae
## 36 o__Oscillospirales f__Acutalibacteraceae
## 37 o__Bacteroidales f__Bacteroidaceae
## 38 o__Enterobacterales f__Pasteurellaceae
## 39 o__unclassified f__unclassified
## 40 o__Haloplasmatales f__Turicibacteraceae
## 41 o__Lachnospirales f__Lachnospiraceae
## 42 o__unclassified f__unclassified
## 43 o__Eubacteriales f__Eubacteriaceae
## 44 o__Lachnospirales f__Lachnospiraceae
## 45 o__Lachnospirales f__Lachnospiraceae
## 46 o__Lachnospirales f__Lachnospiraceae
## 47 o__Lachnospirales f__Lachnospiraceae
## 48 o__Lachnospirales f__Lachnospiraceae
## 49 o__Oscillospirales f__Oscillospiraceae
## 50 o__Lachnospirales f__Lachnospiraceae
## 51 o__Veillonellales f__Veillonellaceae
## 52 o__Veillonellales f__Veillonellaceae
## 53 o__Oscillospirales f__Oscillospiraceae
## 54 o__Peptostreptococcales f__Anaerovoracaceae
## 55 o__Coriobacteriales f__Coriobacteriaceae
## 56 o__Bacteroidales f__Marinifilaceae
## 57 o__Christensenellales f__Christensenellaceae
## 58 o__Lachnospirales f__Lachnospiraceae
## 59 o__Oscillospirales f__Ruminococcaceae
## 60 o__unclassified f__unclassified
## 61 o__Veillonellales f__Dialisteraceae
## 62 o__unclassified f__unclassified
## 63 o__Methanobacteriales f__Methanobacteriaceae
## 64 o__unclassified f__unclassified
## 65 o__Bacteroidales f__Bacteroidaceae
## 66 o__Lactobacillales f__Streptococcaceae
## 67 o__Lachnospirales f__Lachnospiraceae
## 68 o__Erysipelotrichales f__Erysipelotrichaceae
## 69 o__Oscillospirales f__Ruminococcaceae
## 70 o__Bacteroidales f__Barnesiellaceae
## Genus Species
## 1 g__Bifidobacterium s__Bifidobacterium__bifidum
## 2 g__unclassified s__unclassified
## 3 g__Anaerostipes s__Anaerostipes__hadrus
## 4 g__Blautia_A s__unclassified
## 5 g__Streptococcus s__unclassified
## 6 g__Lawsonibacter s__unclassified
## 7 g__Clostridium_M s__Clostridium_M__asparagiforme
## 8 g__Solobacterium s__Solobacterium__moorei
## 9 g__Gemella s__unclassified
## 10 g__Anaerofustis s__Anaerofustis__stercorihominis
## 11 g__Absiella s__unclassified
## 12 g__Blautia_A s__PROV_t__165457
## 13 g__Acutalibacter s__Acutalibacter__timonensis
## 14 g__Blautia s__unclassified
## 15 g__Holdemania s__unclassified
## 16 g__Bacteroides s__unclassified
## 17 g__unclassified s__unclassified
## 18 g__Faecalibacterium s__Faecalibacterium__prausnitzii_K
## 19 g__Coprococcus_A s__Coprococcus_A__catus
## 20 g__unclassified s__unclassified
## 21 g__Lactococcus s__unclassified
## 22 g__unclassified s__unclassified
## 23 g__Bacteroides_B s__Bacteroides_B__vulgatus
## 24 g__Erysipelatoclostridium s__unclassified
## 25 g__Anaerotignum s__unclassified
## 26 g__Eubacterium_E s__unclassified
## 27 g__PROV_t__256727 s__PROV_t__256727
## 28 g__unclassified s__unclassified
## 29 g__Parabacteroides s__Parabacteroides__merdae
## 30 g__unclassified s__unclassified
## 31 g__unclassified s__unclassified
## 32 g__unclassified s__unclassified
## 33 g__Akkermansia s__Akkermansia__muciniphila
## 34 g__Streptococcus s__unclassified
## 35 g__Bacteroides s__unclassified
## 36 g__Anaeromassilibacillus s__PROV_t__258798
## 37 g__Bacteroides s__Bacteroides__thetaiotaomicron
## 38 g__Haemophilus_D s__unclassified
## 39 g__unclassified s__unclassified
## 40 g__Turicibacter s__unclassified
## 41 g__Blautia_A s__PROV_t__220541
## 42 g__unclassified s__unclassified
## 43 g__Eubacterium s__unclassified
## 44 g__Ruminococcus_A s__unclassified
## 45 g__Blautia_A s__unclassified
## 46 g__Blautia_A s__Blautia_A__wexlerae
## 47 g__PROV_t__182732 s__unclassified
## 48 g__unclassified s__unclassified
## 49 g__Intestinimonas s__Intestinimonas__butyriciproducens
## 50 g__Eubacterium_E s__Eubacterium_E__hallii_A
## 51 g__Veillonella s__unclassified
## 52 g__Veillonella s__unclassified
## 53 g__unclassified s__unclassified
## 54 g__unclassified s__unclassified
## 55 g__Collinsella s__unclassified
## 56 g__Odoribacter s__Odoribacter__splanchnicus
## 57 g__PROV_t__186843 s__PROV_t__186843
## 58 g__PROV_t__209626 s__PROV_t__209626
## 59 g__Faecalibacterium s__unclassified
## 60 g__unclassified s__unclassified
## 61 g__Dialister s__PROV_t__4989
## 62 g__unclassified s__unclassified
## 63 g__Methanobrevibacter_A s__unclassified
## 64 g__unclassified s__unclassified
## 65 g__Bacteroides s__Bacteroides__intestinalis
## 66 g__Lactococcus s__unclassified
## 67 g__Blautia_A s__unclassified
## 68 g__Holdemanella s__Holdemanella__biformis
## 69 g__Faecalibacterium s__unclassified
## 70 g__Barnesiella s__Barnesiella__intestinihominis
## Strain
## 1 t__unclassified
## 2 t__unclassified
## 3 t__158681
## 4 t__unclassified
## 5 t__unclassified
## 6 t__unclassified
## 7 t__unclassified
## 8 t__unclassified
## 9 t__unclassified
## 10 t__unclassified
## 11 t__unclassified
## 12 t__165457
## 13 t__145645
## 14 t__unclassified
## 15 t__unclassified
## 16 t__unclassified
## 17 t__unclassified
## 18 t__unclassified
## 19 t__92557
## 20 t__unclassified
## 21 t__unclassified
## 22 t__unclassified
## 23 t__21615
## 24 t__unclassified
## 25 t__unclassified
## 26 t__unclassified
## 27 t__256727
## 28 t__unclassified
## 29 t__unclassified
## 30 t__unclassified
## 31 t__unclassified
## 32 t__unclassified
## 33 t__unclassified
## 34 t__unclassified
## 35 t__unclassified
## 36 t__258798
## 37 t__unclassified
## 38 t__unclassified
## 39 t__unclassified
## 40 t__unclassified
## 41 t__220541
## 42 t__unclassified
## 43 t__unclassified
## 44 t__unclassified
## 45 t__unclassified
## 46 t__unclassified
## 47 t__unclassified
## 48 t__unclassified
## 49 t__unclassified
## 50 t__unclassified
## 51 t__unclassified
## 52 t__unclassified
## 53 t__unclassified
## 54 t__unclassified
## 55 t__unclassified
## 56 t__unclassified
## 57 t__186843
## 58 t__209626
## 59 t__unclassified
## 60 t__unclassified
## 61 t__4989
## 62 t__unclassified
## 63 t__unclassified
## 64 t__unclassified
## 65 t__unclassified
## 66 t__unclassified
## 67 t__unclassified
## 68 t__262551
## 69 t__unclassified
## 70 t__213164.5 Visualization of significant taxa
### functions to plot
make_vis_plots <- function(ps_norm, grouping, tax, plot_type=c('box', 'bar')){
# ps should be a normalized (DESeq or CSS) phyloseq object
# grouping should match the column name in the sample_data
# tax is a taxonomical bin id (ASV) in the counts table to plot
# subset phyloseq object to select ASV of interest
ps_filt=prune_taxa(taxa_names(ps_norm) %in% tax, ps_norm)
# get normalized counts
plot_table<-data.table(otu_table(ps_filt), keep.rownames=TRUE)[rn %in% tax]
# add very small value, min/100000 to 0
plot_table <- melt(plot_table, id.vars='rn')
plot_table$value <- plot_table$value+min(plot_table[value!=0]$value)/100000
# add metadata
groupDT=data.table(data.frame(sample_data(ps_filt)[, c(grouping, 'Within.study.sampling.date')]), keep.rownames=TRUE)
setnames(groupDT, 'rn', 'variable')
plot_table <- merge(plot_table, groupDT, by='variable', all.x=TRUE)
# change variable to general name
setnames(plot_table, grouping, 'Group')
# boxplot
if(plot_type=='box'){
ggplot(data=plot_table, aes(x=Within.study.sampling.date, y = value, fill=Group)) +
geom_boxplot(outlier.color=NA) +
geom_jitter(position=position_jitterdodge(0.2), cex=1.5, color="gray44") +
labs(title =deparse(substitute(ps_norm)), x='', y ='Proportional counts, log scale') +
scale_y_log10() +
scale_fill_manual(values=sgColorPalette)+
theme_minimal() + facet_wrap(~rn, scales='free', ncol=3)+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
} else if (plot_type=='bar'){
plot_table2 <- plot_table[, list(mean_ct=mean(value), sem=sd(value)/sqrt(.N)), by=c('Group', 'Within.study.sampling.date', 'rn')]
ggplot(data=plot_table2, aes(x=Within.study.sampling.date, y =mean_ct, fill=Group)) +
geom_bar(stat='identity', position=position_dodge()) +
geom_errorbar(aes(ymin=mean_ct-sem, ymax=mean_ct+sem), width=0.2, position=position_dodge(0.9))+
labs(title =deparse(substitute(ps_norm)), x='', y ='Proportional counts, 0 to 1 scale') +
#scale_y_log10() +
scale_fill_manual(values=sgColorPalette)+
theme_minimal() + facet_wrap(~rn, scales='free', ncol=3)+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
}
}######BOXPLOT of significant ones
# make significant taxa into one table so that all pvalues retained
significant_tax=NULL
significant_tax <- merge(data.table(deseq_res_P1[[2]], keep.rownames=TRUE)[, list(rn, deseq_P1_adjp=padj)],
data.table(deseq_res_P2[[2]], keep.rownames=TRUE)[, list(rn, deseq_P2_adjp=padj)],
by='rn', all=TRUE)
significant_tax <- merge(significant_tax,
data.table(deseq_res_P3[[2]], keep.rownames=TRUE)[, list(rn, deseq_P3_adjp=padj)],
by='rn', all=TRUE)
significant_tax <- merge(significant_tax,
data.table(bla[[2]], keep.rownames=TRUE)[, list(rn, deseq_timeseries_adjp=padj)],
by='rn', all=TRUE)
significant_tax <- merge(significant_tax,
data.table(zig_res_P1[[2]], keep.rownames=TRUE)[, list(rn, mtgseq_P1_adjp=adjPvalues)],
by='rn', all=TRUE)
significant_tax <- merge(significant_tax,
data.table(zig_res_P2[[2]], keep.rownames=TRUE)[, list(rn, mtgseq_P2_adjp=adjPvalues)],
by='rn', all=TRUE)
significant_tax <- merge(significant_tax,
data.table(zig_res_P3[[2]], keep.rownames=TRUE)[, list(rn, mtgseq_P3_adjp=adjPvalues)],
by='rn', all=TRUE)
significant_tax <- merge(significant_tax,
data.table(zig_res_all[[2]], keep.rownames=TRUE)[, list(rn, mtgseq_timeseries_adjp=adjPvalues)],
by='rn', all=TRUE)
# remove nothing
significant_tax <- significant_tax[rn!='nothing']
# write results
write.csv(significant_tax, file=paste0(output_data, 'Significant_res_deseq_q', deseq_cut, '_mtgseq_q', mtgseq_cut, '.csv'), row.names=FALSE)
datatable(significant_tax)# also, find taxonomical annotations
# NOTE: single ASV may have multiple annotations due to tie hits
#Changing var all_tax_data to tax_table since I don't have this object since I don't have all_tax_data as a object,
datatable(tax_table(ps_not_norm_comp)[rownames(tax_table(ps_not_norm_comp)) %in% significant_tax$rn])4.5.1 DESeq results
## plot
# common by deseq
com_deseq_taxa=significant_tax[!is.na(deseq_P1_adjp) & !is.na(deseq_P2_adjp) & !is.na(deseq_P3_adjp)]
if(nrow(com_deseq_taxa)>0){
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', com_deseq_taxa$rn, 'box'))
} else {
print('no common DESeq significant taxa')
}## [1] "no common DESeq significant taxa"4.5.2 DESeq timeseries results
# deseq timeseries
if(nrow(significant_tax[!is.na(deseq_timeseries_adjp)])>0){
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(deseq_timeseries_adjp)]$rn, 'box'))
# plot bar as well
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(deseq_timeseries_adjp)]$rn, 'bar'))
} else {
print('no DESeq timeseries significant taxa')
}
4.5.3 metagenomeSeq results
# common by metagenomeseq
com_mtgseq_taxa=significant_tax[!is.na(mtgseq_P1_adjp) & !is.na(mtgseq_P2_adjp) & !is.na(mtgseq_P3_adjp)]
if(nrow(com_mtgseq_taxa)>0){
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', com_mtgseq_taxa$rn, 'box'))
} else {
print('no common metagenomeSeq significant taxa')
}
### Meta_genome timeseries results
# mtgseq timeseries
if(nrow(significant_tax[!is.na(mtgseq_timeseries_adjp)])>0){
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[1:6], 'box'))
# plot bar as well
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[1:6], 'bar'))
} else {
print('no Mtgseq timeseries significant taxa')
}
if(nrow(significant_tax[!is.na(mtgseq_timeseries_adjp)])>0){
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[7:12], 'box'))
# plot bar as well
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[7:12], 'bar'))
} else {
print('no Mtgseq timeseries significant taxa')
}
if(nrow(significant_tax[!is.na(mtgseq_timeseries_adjp)])>0){
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[13:18], 'box'))
# plot bar as well
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[13:18], 'bar'))
} else {
print('no Mtgseq timeseries significant taxa')
}
if(nrow(significant_tax[!is.na(mtgseq_timeseries_adjp)])>0){
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[19:24], 'box'))
# plot bar as well
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[19:24], 'bar'))
} else {
print('no Mtgseq timeseries significant taxa')
}
if(nrow(significant_tax[!is.na(mtgseq_timeseries_adjp)])>0){
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[25:27], 'box'))
# plot bar as well
print(make_vis_plots(ps_TSS_norm_pass_min_postDD_sup003, 'phenotype', significant_tax[!is.na(mtgseq_timeseries_adjp)]$rn[25:27], 'bar'))
} else {
print('no Mtgseq timeseries significant taxa')
}
5 CCA and visualization
Compare resulting amplicon data between and within sample types by canonical correlation analysis, regression profiling, and visualization (e.g. non-metric multi-dimensional scaling [NMDS], principle coordinates of analysis, principle component analysis).
5.1 Constrained by status A or N
plotting_phenotype_consPcoA <- function(ps,title){
fam_6<-names(table(sample_data(ps)$Family.group.ID)[table(sample_data(ps)$Family.group.ID) == 6])
ps_6fam<-prune_samples(sample_data(ps)$Family.group.ID %in% fam_6,ps )
ps_pcoa_ord <- ordinate(
physeq = ps_6fam,
method = "CAP",
distance = "bray",
formula = ~ phenotype
)
p<-plot_ordination(
physeq = ps_6fam,
ordination = ps_pcoa_ord,
color = "phenotype",
axes = c(1,2),
title= paste("Constrained PcoA",title,"ordinated by phenotype with all timepoints")
) +
geom_point( size = 2) +
scale_color_manual(values=sgColorPalette)+
theme_minimal()+
theme(text = element_text(size =10), plot.title = element_text(size=10))
#sum_pcoA_DesEq<-summary(ps_pcoa_ord)
erie_bray_sum_pcoA <- phyloseq::distance(ps, method = "bray")
sampledf <- data.frame(sample_data(ps))
beta_di<-betadisper(erie_bray_sum_pcoA, sampledf$Family.group.ID)
to_return<-list()
to_return[[1]]<-p
to_return[[2]]<-beta_di
return(to_return)
}
#With Deseq
DeSeq_distance<-plotting_phenotype_consPcoA(ps_DeSeq_norm_pass_min_postDD_sup003, "Deseq")
# plot
DeSeq_distance[[1]]
#same with CSS
CSS_distance<-plotting_phenotype_consPcoA(ps_CSS_norm_pass_min_postDD_sup003, "CSS")
# plot
CSS_distance[[1]]
5.2 Constrained by family
#plotting
#Now we have: 803 taxa and 559 samples
#Looking at the family fro the complete set of samples
#Keeping the same ordination but filtering to the families with only 6 point to help vizualize the plot
#Looking at NORMALIZATION
plotting_Fam_consPcoA <- function(ps,title){
fam_6<-names(table(sample_data(ps)$Family.group.ID)[table(sample_data(ps)$Family.group.ID) == 6])
ps_6fam<-prune_samples(sample_data(ps)$Family.group.ID %in% fam_6,ps )
sample_data(ps_6fam)$Family.group.ID <- paste0('fam', as.character(sample_data(ps_6fam)$Family.group.ID))
ps_pcoa_ord <- ordinate(
physeq = ps_6fam,
method = "CAP",
distance = "bray",
formula = ~ Family.group.ID
)
p<-plot_ordination(
physeq = ps_6fam,
ordination = ps_pcoa_ord,
color = "Family.group.ID",
axes = c(1,2),
title= paste("Constrained PcoA",title,"ordinated by families with all timepoints")
) +
geom_point( size = 2) +
theme_minimal()+
theme(text = element_text(size =10), plot.title = element_text(size=10), legend.position='none')
#sum_pcoA_DesEq<-summary(ps_pcoa_ord)
erie_bray_sum_pcoA <- phyloseq::distance(ps, method = "bray")
sampledf <- data.frame(sample_data(ps))
beta_di<-betadisper(erie_bray_sum_pcoA, sampledf$Family.group.ID)
to_return<-list()
to_return[[1]]<-p
to_return[[2]]<-beta_di
return(to_return)
}
#With Deseq
DeSeq_distance<-plotting_Fam_consPcoA(ps_DeSeq_norm_pass_min_postDD_sup003, "Deseq")
# plot
DeSeq_distance[[1]]
#same with CSS
CSS_distance<-plotting_Fam_consPcoA(ps_CSS_norm_pass_min_postDD_sup003, "CSS")
# plot
CSS_distance[[1]]
#the distance in those plot?
#average_distance_to_median
#pdf(file=paste0(output_data, "Figures/Distance_DeSeq_CSS_", Sys.Date(), ".pdf"))
boxplot(DeSeq_distance[[2]]$distances,CSS_distance[[2]]$distances, names=c("DeSeq", "CSS"),
xlab = "Type of Normalization", ylab = "Distance on Component 1 & 2", main ="Intragroup distance for each family")
#dev.off()6 Diversity
Characterize and assess the diversity of each sample, and evaluate the extent of dissimilarity between the cohorts
6.1 Alpha diversity
ER <- estimate_richness(ps_not_norm_comp, measures=c("Observed", "Chao1", "Shannon"))
ER <- cbind(ER, sample_data(ps_not_norm_comp)[row.names(ER), c("phenotype", "Family.group.ID", "Within.study.sampling.date")])
ER <- data.table(ER, keep.rownames = TRUE)
ER <- melt(ER, id.vars=c('rn', 'phenotype', "Family.group.ID", "Within.study.sampling.date"))
# plot
ggplot(data=ER[variable!='se.chao1'], aes(x=phenotype, y=value, fill=phenotype))+
geom_boxplot(width=0.7, outlier.colour='white')+
geom_jitter(size=1, position=position_jitter(width=0.1))+
xlab('')+ylab('')+
scale_fill_manual(values=sgColorPalette)+
theme_minimal()+facet_wrap(~variable, scales='free')
# run t-test to check significance
ttest=NULL
for(alphad in c('Observed', 'Chao1', 'Shannon')){
ttest_res=t.test(value ~ phenotype, data=ER[variable==alphad], var.equal=TRUE)
ttest=rbindlist(list(ttest, data.table(alpha_index=alphad, pvalue=ttest_res$p.value)))
}
pander(ttest)| alpha_index | pvalue |
|---|---|
| Observed | 0.2772 |
| Chao1 | 0.2772 |
| Shannon | 0.5052 |
6.2 Beta diversity
#Let's do a PcoA #not much differences
GP.ord <- ordinate(ps_DeSeq_norm_pass_min_postDD_sup003, "PCoA", "bray")
p2 = plot_ordination(ps_DeSeq_norm_pass_min_postDD_sup003, GP.ord, type="samples", color="phenotype") +
geom_point( size = 1)+
scale_color_manual(values=sgColorPalette)+
theme_minimal()
p2
7 PERMANOVA
non- parametric statistical approaches (ANOSIM, ADONIS, ANOVA, PERMANOVA, etc.) will be employed to determine the significance of noteworthy factors, such as digital phenotype, probiotic and/or antibiotic use
7.1 PERMANOVA test
permanova <- function(physeq, factorName, ifnumeric, pmt=999){
set.seed(1)
braydist = phyloseq::distance(physeq, "bray")
form <- as.formula(paste("braydist ~ ", c(factorName), sep = ""))
metaDF=data.frame(sample_data(physeq)[, as.character(factorName)])
# if numerical variable, make sure the class
if(ifnumeric){
metaDF[, factorName] <- as.numeric(metaDF[, factorName])
factor_class='numeric'
} else {
factor_class='categorical'
}
perm <- adonis(form, permutations = pmt, metaDF)
permDT=data.table(Variable=factorName,
FactorClass=factor_class,
TotalN=perm$aov.tab['Total','Df']+1,
R2=perm$aov.tab[factorName, 'R2'],
pvalue=perm$aov.tab[factorName,'Pr(>F)'][1])
return(permDT)
}
#betadispersion
#we keep only the cateory selected above as relevant
tmp_metadat<-metadata_ok[,c(num_cat,fac_cat)]
#additionnal error to remove: not enough sample:
tmp_metadat<-tmp_metadat[,-which(colnames(tmp_metadat) %in% c("Number.of.pet.reptiles","Number.of.pet.horses", "Pet.horse"))]
#additionnal error to remove: filled with only NA or one factor, cant do permutest on it due to adonis function requirements
col_levels<-sapply(tmp_metadat, levels)
col_levelscount<-sapply(col_levels, length)
tmp_metadat_1 <- tmp_metadat
#Since there are no numerics based on code below, will drop all that dont have 2 or more levels
#tmp_metadat[,which(sapply(tmp_metadat, class) == "numeric")]
tmp_metadat <- tmp_metadat[,which(col_levelscount >= 2)]
set.seed(1)
pval_factors_diper=c()
nb_samples_disper=c()
for (i in 1:length(tmp_metadat)){
#cat (i,"\t")
test_map<-tmp_metadat[!is.na(tmp_metadat[,i]) & tmp_metadat[,i] != "" ,]
ps.tmp<-copy(ps_DeSeq_norm_pass_min_postDD_sup003)
sample_data(ps.tmp) <- test_map
df_metadata <- data.frame(sample_data(ps.tmp))
df_metadata<-df_metadata[df_metadata[,colnames(test_map)[i]] != "",]
# use prune_samples instead of subset_samples
keepid=!is.na(get_variable(ps.tmp, colnames(test_map)[i])) &
get_variable(ps.tmp, colnames(test_map)[i])!='' &
get_variable(ps.tmp, colnames(test_map)[i])!='NA'
ps.tmp <- prune_samples(keepid, ps.tmp)
#ps.tmp <- subset_samples(ps.tmp, colnames(test_map)[i] !="")
tmp_nb_samples<-dim(otu_table(ps.tmp))[2]
OTU_tables_bray <- phyloseq::distance(ps.tmp, method = "bray")
beta <- betadisper(OTU_tables_bray, df_metadata[,colnames(test_map)[i]])
tmp<-permutest(beta)
tmp<-tmp$tab$`Pr(>F)`[1]
pval_factors_diper<-c(pval_factors_diper,tmp)
nb_samples_disper<-c(nb_samples_disper,tmp_nb_samples)}
#correct the p.value
names(pval_factors_diper)<-colnames(tmp_metadat)
pval_factors_diper<-p.adjust(pval_factors_diper, method = "fdr")
to_remove_betadis<-names(pval_factors_diper)[pval_factors_diper<0.05]
# list of permanova variables
#meta_cat <- tibble(col_levelscount >= 2, colnames(tmp_metadat_1), sapply(tmp_metadat_1, class))
#rownames(meta_cat) <- colnames(tmp_metadat_1)
#colnames(meta_cat) <- c("permanova", "varname", "type")
#meta_cat$type <- gsub("factor", "Categorical", meta_cat$type)
#meta_cat$type <- gsub("numerical", "Continuous", meta_cat$type)
#meta_cat file listed phenotype as false for permanova, but I will add it back in)
meta_cat$permanova[which(meta_cat$varname == "phenotype")] <- "Categorical"
permanova_var=meta_cat[which(meta_cat$permanova!=FALSE),]
permanova_var$permanova[which(permanova_var$varname %in% c(dict_1_items, dict_2_items, "Stool.frequency"))] <- rep("Continuous", length(permanova_var$permanova[which(permanova_var$varname %in% c(dict_1_items, dict_2_items, "Stool.frequency"))]))
set.seed(1)
permanova_res=NULL
for(j in 1:nrow(permanova_var)){
#print(factorName1)
#pander(table(sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)[, factorName1]))
# variable name (as.characteradded)
var_name=as.character(permanova_var$varname[j])
# remove all NAs
keepid=!is.na(get_variable(ps_DeSeq_norm_pass_min_postDD_sup003, var_name)) &
get_variable(ps_DeSeq_norm_pass_min_postDD_sup003, var_name)!='NA' &
get_variable(ps_DeSeq_norm_pass_min_postDD_sup003, var_name)!=''
tmp_ps <- prune_samples(keepid, ps_DeSeq_norm_pass_min_postDD_sup003)
# Check if there is more than 1 values (categories)
if(uniqueN(sample_data(tmp_ps)[, var_name])>1){
# if categorical
if(permanova_var$permanova[j]=='Categorical'){
# run permanova only if there are more than 1 groups
p <- permanova(tmp_ps, factorName=var_name, ifnumeric=FALSE, pmt=999)
permanova_res=rbindlist(list(permanova_res, p))
rm(p)
}
# if continuous
if(permanova_var$permanova[j]=='Continuous'){
p <- permanova(tmp_ps, factorName=var_name, ifnumeric=TRUE, pmt=999)
permanova_res=rbindlist(list(permanova_res, p))
rm(p)
}
}
rm(var_name)
}
# write
write.csv(permanova_res, file=paste0(output_data, 'PERMANOVA.csv'), row.names=FALSE)
# total number of variables tested
uniqueN(permanova_res$Variable)[1] 123
# Factor class
pander(table(permanova_res$FactorClass))| categorical | numeric |
|---|---|
| 91 | 32 |
# number of significant variables
uniqueN(permanova_res[pvalue<permanova_pcut]$Variable)[1] 113
#and now removing the ones with betadispersion significant
impacting_compo<-setdiff(permanova_res[pvalue<permanova_pcut]$Variable, to_remove_betadis)
#and now the ones also significant between the two cohorts
impacting_compo<-impacting_compo[impacting_compo %in% c(names(all_chisquare))]
permanova_res<- permanova_res[permanova_res$Variable %in% impacting_compo,]
#removing LR predictions since those are essentially an indicator of phenotype and not confounding variables
permanova_res<-permanova_res[-which(permanova_res$Variable %in% c("LR10.probability.ASD..M3.", "LR5.probability.ASD..M3.", "LR6.probability.ASD..M3." , "LR10.prediction..M3.", "LR10.probability.not.ASD..M3.", "LR5.probability.not.ASD..M3." , "LR5.prediction..M3.", "LR6.prediction..M3." , "LR6.probability.not.ASD..M3.")),]
# sort
permanova_res <- permanova_res[order(R2, decreasing=TRUE)]
datatable(permanova_res)write.csv(permanova_res, file=paste0(output_data, 'PERMANOV_betadis_imp_corhort.csv'), row.names=FALSE)7.2 Visualize permanova significant variables on PCoA
# function to plot PCoA, only for higher R2 value
imp_factors<-permanova_res$Variable[permanova_res$R2 > 0.01]
imp<-list()
for (i in 1:length(imp_factors)){
if(anyNA(map[,imp_factors[i]]) == FALSE){
imp[i] <- imp_factors[i]
}
}
impforpcoa<-unlist(imp)
add<-paste(impforpcoa, collapse = " + ")
#copy and paste form variable below into formula for pcoa for convenience (not sure why it does not work as an input for formula, but copy/paste as text works)
form<- as.formula(paste0("~ ", add))
#ordination formula only working with one variable in formula...
ps_pcoa <- ordinate(
physeq = ps_DeSeq_norm_pass_min_postDD_sup003,
method = "CAP",
distance = "bray",
#Did not include Toilet.cover and Meat/Seafood Longitudinal, Fruit..consumption.frequency...longitudinal. and LR10.prediction..M3. due to NA missing values which does not allow for ordination
formula = ~Stool.frequency + Age..months. + Age..years. + Vitamin.D..consumption.frequency.)
title_prep<-impforpcoa
to_plot=list()
for (i in 1:4){
to_plot[[i]] <- plot_ordination(
physeq = ps_DeSeq_norm_pass_min_postDD_sup003,
ordination = ps_pcoa,
color = title_prep[i],
axes = c(1,2),
title=title_prep[i]
) +
geom_point( size = 0.5) +
theme(text = element_text(size =20), plot.title = element_text(size=15))
}
to_plot[[5]]<-plot_ordination(physeq = ps_DeSeq_norm_pass_min_postDD_sup003, ordination = ps_pcoa, type="taxa",title ="Taxa") + theme(text = element_text(size =15))
lay <- rbind(c(1),
c(2),
c(3),
c(4),
c(5))
#pdf(paste0(output_data,"confounding_factors.pdf",width=16,height=40))
grid.arrange(grobs = to_plot, layout_matrix = lay)
top_potential_confounds <- imp_factors
#dev.off()#Let's have a look at the plot
plot_ordination(physeq = ps_DeSeq_norm_pass_min_postDD_sup003, ordination = ps_pcoa, type="taxa",title ="Taxa") + theme(text = element_text(size =8))
#ok let's try to find the spcies that show some importance in this PCA
taxa.to.select<-vegan::scores(ps_pcoa)$species
#now plot it with no name for visibilty
rownames(taxa.to.select)<-c()
s.arrow(taxa.to.select) #the taxa that influence the most the plots are above 0.25
taxa.to.select.to.rem<-vegan::scores(ps_pcoa)$species[abs(vegan::scores(ps_pcoa)$species[,1])>0.1 | abs(vegan::scores(ps_pcoa)$species[,2])>0.1,]
#any overlap with the 5 important?
rownames(bla[[2]]) %in% taxa.to.select.to.rem #NOPE!## [1] FALSE FALSE7.3 Comparison of Variance between Subjects and between Phenotypes
#Comparing variance w/ avg difference in distance
tmpps <- prune_samples((ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Family.group.ID == "1"), ps_DeSeq_norm_pass_min_postDD_sup003)
tmppsA <- prune_samples(tmpps@sam_data$phenotype == "A", tmpps)
tmppsN <- prune_samples(tmpps@sam_data$phenotype == "N", tmpps)
A<-distance(tmppsA, "bray", type = "samples")
ave_distanceA=ave(c(A[1],A[2],A[3]))[1]
N<-distance(tmppsN, "bray", type = "samples")
ave_distanceN=ave(c(N[1],N[2],N[3]))[1]
tab<-tibble(ave_distanceA, ave_distanceN, tmpps@sam_data$Family.group.ID[1])
colnames(tab) <- c("AvgDistanceAut", "AvgDistanceNeu", "Family")
for(i in unique(ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Family.group.ID)){
tmpps <- prune_samples((ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Family.group.ID == i), ps_DeSeq_norm_pass_min_postDD_sup003)
tmppsA <- prune_samples(tmpps@sam_data$phenotype == "A", tmpps)
tmppsN <- prune_samples(tmpps@sam_data$phenotype == "N", tmpps)
A<-distance(tmppsA, "bray", type = "samples")
ave_distanceA=ave(c(A[1],A[2],A[3]))[1]
N<-distance(tmppsN, "bray", type = "samples")
ave_distanceN=ave(c(N[1],N[2],N[3]))[1]
tabtmp<-tibble(ave_distanceA, ave_distanceN, tmpps@sam_data$Family.group.ID[1])
colnames(tabtmp) <- c("AvgDistanceAut", "AvgDistanceNeu", "Family")
tab<-rbind(tab, tabtmp)
}
# run tests to check significance
taba<-tibble(tab$AvgDistanceAut, rep("A", length(tab$AvgDistanceAut)), tab$Family)
colnames(taba) <- c("AvgDistanceDiff_btwnTimepoints","phenotype", "Family" )
tabn<-tibble(tab$AvgDistanceNeu, rep("N", length(tab$AvgDistanceNeu)), tab$Family)
colnames(tabn) <- c("AvgDistanceDiff_btwnTimepoints","phenotype" , "Family")
finaltab2<-rbind(tabn, taba)
p <- ggplot(finaltab2, aes(x=phenotype, y=AvgDistanceDiff_btwnTimepoints)) +
geom_boxplot()
p + geom_jitter(shape=16, position=position_jitter(0.2))
# run tests to check significance
shapiro.test(finaltab2$AvgDistanceDiff_btwnTimepoints) #not normal we need a reanking test##
## Shapiro-Wilk normality test
##
## data: finaltab2$AvgDistanceDiff_btwnTimepoints
## W = 0.87065, p-value = 3.487e-09wilcox.test(AvgDistanceDiff_btwnTimepoints ~ phenotype, data=finaltab2, var.equal=FALSE)##
## Wilcoxon rank sum test with continuity correction
##
## data: AvgDistanceDiff_btwnTimepoints by phenotype
## W = 1948, p-value = 0.6354
## alternative hypothesis: true location shift is not equal to 0#not significant
#paired wilcoxon
tmpps <- prune_samples((ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Family.group.ID == "1"), ps_DeSeq_norm_pass_min_postDD_sup003)
tmppsA <- prune_samples(tmpps@sam_data$phenotype == "A", tmpps)
tmppsN <- prune_samples(tmpps@sam_data$phenotype == "N", tmpps)
A<-distance(tmppsA, "bray", type = "samples")
distanceA=c(A[1],A[2],A[3])
N<-distance(tmppsN, "bray", type = "samples")
distanceN=c(N[1],N[2],N[3])
tab<-tibble(distanceA, distanceN, rep(tmpps@sam_data$Family.group.ID[1], length(distanceA)), c("Timepoint 1-2", "Timepoint 1-3", "Timepoint 2-3"))
colnames(tab) <- c("DistanceAut", "DistanceNeu", "Family", "Timepoint")
for(i in unique(ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Family.group.ID)[c(-1)]){
tmpps <- prune_samples((ps_DeSeq_norm_pass_min_postDD_sup003@sam_data$Family.group.ID == i), ps_DeSeq_norm_pass_min_postDD_sup003)
tmppsA <- prune_samples(tmpps@sam_data$phenotype == "A", tmpps)
tmppsN <- prune_samples(tmpps@sam_data$phenotype == "N", tmpps)
A<-distance(tmppsA, "bray", type = "samples")
distanceA=c(A[1],A[2],A[3])
N<-distance(tmppsN, "bray", type = "samples")
distanceN=c(N[1],N[2],N[3])
tabtmp<-tibble(distanceA, distanceN, rep(tmpps@sam_data$Family.group.ID[1], length(distanceA)), c("Timepoint 1-2", "Timepoint 1-3", "Timepoint 2-3"))
colnames(tabtmp) <- c("DistanceAut", "DistanceNeu", "Family", "Timepoint")
tab<-rbind(tab, tabtmp)
}
tab_w_tp <- tab
# run tests to check significance
taba<-tibble(tab$DistanceAut, rep("A", length(tab$DistanceAut)), tab$Family, tab$Timepoint)
colnames(taba) <- c("Distances_btwnTimepoints","phenotype", "Family" , "Timepoint")
tabn<-tibble(tab$DistanceNeu, rep("N", length(tab$DistanceNeu)), tab$Family, tab$Timepoint)
colnames(tabn) <- c("Distances_btwnTimepoints","phenotype", "Family" , "Timepoint")
finaltab3<-rbind(tabn, taba)
p <- ggplot(finaltab3, aes(x=phenotype, y=Distances_btwnTimepoints)) +
geom_boxplot()
p + geom_jitter(shape=16, position=position_jitter(0.2))
# run tests to check significance
wilcox.test(tab$DistanceAut, tab$DistanceNeu, paired = TRUE)##
## Wilcoxon signed rank test with continuity correction
##
## data: tab$DistanceAut and tab$DistanceNeu
## V = 9608, p-value = 0.4028
## alternative hypothesis: true location shift is not equal to 0#still not significant
#Will do permutations
#with avgs
permutation_meandist_gen<-function(x){
ptab <- x
ptab$permu_label<- ptab$phenotype[shuffle(ptab$phenotype)]
mean(ptab$AvgDistanceDiff_btwnTimepoints[which(ptab$permu_label == "A")]) -
mean(ptab$AvgDistanceDiff_btwnTimepoints[which(ptab$permu_label == "N")])
}
permu_means<-replicate(1000, permutation_meandist_gen(finaltab2))
diff.means<-mean(finaltab2$AvgDistanceDiff_btwnTimepoints[which(finaltab2$phenotype == "A")]) -
mean(finaltab2$AvgDistanceDiff_btwnTimepoints[which(finaltab2$phenotype == "N")])
sig <- sum(permu_means > diff.means)
hist(permu_means) 
# with raw values
permutation_meandist_gen<-function(x){
ptab <- x
ptab$permu_label<- ptab$phenotype[shuffle(ptab$phenotype)]
mean(ptab$Distances_btwnTimepoints[which(ptab$permu_label == "A")]) -
mean(ptab$Distances_btwnTimepoints[which(ptab$permu_label == "N")])
}
set.seed(1)
permu_means<-replicate(1000, permutation_meandist_gen(finaltab3))
diff.means<-mean(finaltab3$Distances_btwnTimepoints[which(finaltab3$phenotype == "A")]) -
mean(finaltab3$Distances_btwnTimepoints[which(finaltab3$phenotype == "N")])
sig <- as.numeric(sum(permu_means >= diff.means))
pval<-sig/1000
pval## [1] 0.414{hist(permu_means)
abline(v = diff.means, col = "red")}
{plot(density(permu_means))
abline(v = diff.means, col = "red")}
#organized by family
permutation_meandist_gen_by_fam<-function(x){
ptab <- x
tmp <- ptab[which(ptab$Family == ptab$Family[1]),]
tmp$permu_label<- tmp$phenotype[shuffle(tmp$phenotype)]
ptab_all <- tmp
for (i in 2:length(unique(ptab$Family))){
tmp <- ptab[which(ptab$Family == unique(ptab$Family)[i]),]
tmp$permu_label<- tmp$phenotype[shuffle(tmp$phenotype)]
ptab_all <- rbind(ptab_all, tmp)
}
mean(ptab_all$Distances_btwnTimepoints[which(ptab_all$permu_label == "A")]) -
mean(ptab_all$Distances_btwnTimepoints[which(ptab_all$permu_label == "N")])
}
set.seed(1)
permu_means_by_fam<-replicate(1000, permutation_meandist_gen_by_fam(finaltab3))
sig <- as.numeric(sum(permu_means >= diff.means))
pval_by_fam<-sig/1000
pval_by_fam## [1] 0.414{hist(permu_means_by_fam)
abline(v = diff.means, col = "red")}
{plot(density(permu_means_by_fam))
abline(v = diff.means, col = "red")}
# Now with difference between A and N at each time point, then taking mean
#Since they are in order by family and timepoint, I can subtract across
#view to make sure
#finaltab3[which(finaltab3$phenotype == "A"),]
#finaltab3[which(finaltab3$phenotype == "N"),]
# First just by timepoint
permutation_meandist_gen_by_fam_diff<-function(x){
ptab <- x
tmp <- ptab[which(ptab$Family == ptab$Family[1]),]
tmp$permu_label<- tmp$phenotype[shuffle(tmp$phenotype)]
ptab_all <- tmp
for (i in 2:length(unique(ptab$Family))){
tmp <- ptab[which(ptab$Family == unique(ptab$Family)[i]),]
tmp$permu_label<- tmp$phenotype[shuffle(tmp$phenotype)]
ptab_all <- rbind(ptab_all, tmp)
}
mean(ptab_all$Distances_btwnTimepoints[which(ptab_all$permu_label == "A")]-
ptab_all$Distances_btwnTimepoints[which(ptab_all$permu_label == "N")])
}
fintab_1_2<-finaltab3[which(finaltab3$Timepoint == "Timepoint 1-2"),]
fintab_1_3<-finaltab3[which(finaltab3$Timepoint == "Timepoint 1-3"),]
fintab_2_3<-finaltab3[which(finaltab3$Timepoint == "Timepoint 2-3"),]
set.seed(1)
permu_means_by_fam12<-replicate(1000, permutation_meandist_gen_by_fam_diff(fintab_1_2))
mean_difference<-mean(fintab_1_2$Distances_btwnTimepoints[which(fintab_1_2$phenotype == "A")] -fintab_1_2$Distances_btwnTimepoints[which(fintab_1_2$phenotype == "N")])
sig <- as.numeric(sum(permu_means_by_fam12 >= mean_difference))
pval_by_fam12<-sig/1000
set.seed(1)
permu_means_by_fam13<-replicate(1000, permutation_meandist_gen_by_fam_diff(fintab_1_3))
mean_difference<-mean(fintab_1_3$Distances_btwnTimepoints[which(fintab_1_3$phenotype == "A")] -fintab_1_3$Distances_btwnTimepoints[which(fintab_1_3$phenotype == "N")])
sig <- as.numeric(sum(permu_means_by_fam13 >= mean_difference))
pval_by_fam13<-sig/1000
set.seed(1)
permu_means_by_fam23<-replicate(1000, permutation_meandist_gen_by_fam_diff(fintab_2_3))
mean_difference<-mean(fintab_2_3$Distances_btwnTimepoints[which(fintab_2_3$phenotype == "A")] -fintab_2_3$Distances_btwnTimepoints[which(fintab_2_3$phenotype == "N")])
sig <- as.numeric(sum(permu_means_by_fam23 >= mean_difference))
pval_by_fam23<-sig/1000
#All_together
permutation_meandist_gen_by_fam_diff_all<-function(x){
ptab <- x
tmp <- ptab[which(ptab$Family == ptab$Family[1]),]
tmp_time <- tmp[which(tmp$Timepoint == unique(tmp$Timepoint)[1]),]
tmp_time$permu_label<- tmp_time$phenotype[shuffle(tmp_time$phenotype)]
tmp_time_all <- tmp_time
for (b in 2:3) {
tmp_time <- tmp[which(tmp$Timepoint == unique(tmp$Timepoint)[b]),]
tmp_time$permu_label<- tmp_time$phenotype[shuffle(tmp_time$phenotype)]
tmp_time_all <- rbind(tmp_time_all, tmp_time)
}
ptab_all <-tmp_time_all
for (i in 2:length(unique(ptab$Family))){
tmp <- ptab[which(ptab$Family == unique(ptab$Family)[i]),]
tmp_time <- tmp[which(tmp$Timepoint == unique(tmp$Timepoint)[1]),]
tmp_time$permu_label<- tmp_time$phenotype[shuffle(tmp_time$phenotype)]
tmp_time_all <- tmp_time
for (b in 2:3) {
tmp_time <- tmp[which(tmp$Timepoint == unique(tmp$Timepoint)[b]),]
tmp_time$permu_label<- tmp_time$phenotype[shuffle(tmp_time$phenotype)]
tmp_time_all <- rbind(tmp_time_all, tmp_time)
}
ptab_all <- rbind(ptab_all, tmp_time_all)
}
mean(ptab_all$Distances_btwnTimepoints[which(ptab_all$permu_label == "A")]-
ptab_all$Distances_btwnTimepoints[which(ptab_all$permu_label == "N")])
}
permu_means_by_fam_shuffled_while_maintaining_tp<-replicate(1000, permutation_meandist_gen_by_fam_diff(finaltab3))
mean_difference<-mean(finaltab3$Distances_btwnTimepoints[which(finaltab3$phenotype == "A")] -finaltab3$Distances_btwnTimepoints[which(finaltab3$phenotype == "N")])
sig <- as.numeric(sum(permu_means_by_fam_shuffled_while_maintaining_tp >= mean_difference))
pval_by_fam_all<-sig/1000
{plot(density(permu_means_by_fam_shuffled_while_maintaining_tp))
abline(v = mean_difference, col = "red")}
7.4 Without NA points
# function to plot PCoA without NA points
wo_na_pcoa <- function(ps, pvar, ord_res){
# ord_res: ordinated object
keepid=!is.na(get_variable(ps, pvar)) &
get_variable(ps, pvar)!='NA' &
get_variable(ps, pvar)!=''
tmp_ps <- prune_samples(keepid, ps)
# get subset counts and metadata together
DF <- cbind(ord_res$vectors[row.names(sample_data(tmp_ps)), 1:2], sample_data(tmp_ps)[, pvar])
setnames(DF, pvar, 'testvar')
# get eigenvalues
eig=(ord_res$values$Eigenvalues/sum(ord_res$values$Eigenvalues))[1:2]*100
p <- ggplot(data=DF, aes(x=Axis.1, y=Axis.2, colour=testvar))+
geom_point(size=2)+
ggtitle(pvar)+
xlab(paste0('Axis.1 [', format(eig[1], digits=3), '%]'))+
ylab(paste0('Axis.2 [', format(eig[2], digits=3), '%]'))+
theme_minimal()+
theme(legend.title=element_blank(), legend.position="bottom")
print(p)
}7.4.1 Looking into confounding variables
#Hard to find a confounding variable in impfactors that does not have a lot of NAs (no NAs required for DESEQ2) will put through metagenomeseq
#Function Updated with Altered formula for confound var
runDESeq_time_confound <- function(ps, dcut, confound){
diagdds = phyloseq_to_deseq2(ps, as.formula(paste0("~ ", confound, "+ Within.study.sampling.date")))
diagdds <- estimateSizeFactors(diagdds, type = "poscounts")
diagdds <- DESeq(diagdds,fitType="parametric", betaPrior = FALSE)
#resultsNames(diagdds): to determine the constrast
res = results(diagdds, contrast = c(confound, levels(map[,confound])[1], levels(map[,confound])[2]))
res$padj[is.na(res$padj)] = 1
sig <- res[res$padj < dcut,]
if (dim(sig)[1] == 0)
{sigtab<- as.data.frame(1, row.names="nothing")
colnames(sigtab) <- 'padj'}
else
{
sigtab <- data.frame(sig)
}
return(list(res, sigtab))
}
#Function Updated with Altered formula for confound var
run_metagenom_seq_confound<-function(ps,maxit, mcut, confound){
p_metag<-phyloseq_to_metagenomeSeq(ps)
#filtering at least 4 samples
p_metag= cumNorm(p_metag, p=0.75)
normFactor =normFactors(p_metag)
normFactor =log2(normFactor/median(normFactor) + 1)
#mod = model.matrix(~ASDorNeuroT +PairASD+ normFactor)
mod = model.matrix(as.formula(paste0("~ ", confound, "+ Within.study.sampling.date +normFactor")), data = pData(p_metag))
settings =zigControl(maxit =maxit, verbose =FALSE)
#settings =zigControl(tol = 1e-5, maxit = 30, verbose = TRUE, pvalMethod = 'bootstrap')
fit =fitZig(obj = p_metag, mod = mod, useCSSoffset = FALSE, control = settings)
#Note: changed fit$taxa to fit@taxa in light of error (probably from newer metagenomeseq ver.)
res_fit<-MRtable(fit, number = length(fit@taxa))
res_fit_nonfiltered <- copy(res_fit)
res_fit<-res_fit[res_fit$adjPvalues<mcut,]
#finally remove the ones that are not with enough samples
#mean_sample<-mean(calculateEffectiveSamples(fit))
#res_fit<-res_fit[res_fit$`counts in group 0` & res_fit$`counts in group 1` > mean_sample,]
Min_effec_samp<-calculateEffectiveSamples(fit)
Min_effec_samp<-Min_effec_samp[ names(Min_effec_samp) %in% rownames(res_fit)] #####there is a bug here
#manually removing the ones with "NA"
res_fit<-res_fit[grep("NA",rownames(res_fit), inv=T),]
res_fit$Min_sample<-Min_effec_samp
res_fit<-res_fit[res_fit$`+samples in group 0` >= Min_effec_samp & res_fit$`+samples in group 1` >= Min_effec_samp,]
return(list(res_fit_nonfiltered, res_fit))
}
cat_confounds<-permanova_res$Variable[permanova_res$FactorClass == "categorical"]
num_confounds<-permanova_res$Variable[permanova_res$FactorClass == "numeric"]
#Remove var with more than 40 NAs
toomanyNAs<-list()
for (i in 1:length(cat_confounds)){
tmp<-is.na(filtered_ps003@sam_data[,cat_confounds[i]])
if (length(rownames(tmp)[which(tmp == TRUE)]) >=40) {
toomanyNAs[i] <-cat_confounds[i]
}
}
cat_confounds<-cat_confounds[-which(cat_confounds %in% unlist(toomanyNAs))]
confound <- cat_confounds[1]
#some are listed as logical
write.csv(sample_data(filtered_ps003), "sam_data.csv")
map<-read.csv("sam_data.csv")
map[,confound] <- as.factor(map[,confound])
rownames(map) <- map$Biospecimen.Barcode
sample_data(filtered_ps003) <- map
filtered_ps003NAout<-prune_samples(!is.na(map[,confound]), filtered_ps003)
if(levels(map[,confound]) == 2){
deseqcon<-runDESeq_time_confound(filtered_ps003NAout, deseq_cut, confound = confound)
mtgcon<-run_metagenom_seq_confound(filtered_ps003NAout, 30, mtgseq_cut, confound = confound)
affected_taxa <-c(rownames(mtgcon[[2]]), row.names(deseqcon[2][[1]]) )
} else{
mtgcon<-run_metagenom_seq_confound(filtered_ps003NAout, 30, mtgseq_cut, confound = confound)
affected_taxa <-rownames(mtgcon[[2]])
}
#Run Deseq2 or Metagenomeseq on categorical confounds
for (i in 2:length(cat_confounds)) {
confound <- cat_confounds[i]
#some are listed as logical
write.csv(sample_data(filtered_ps003), "sam_data.csv")
map<-read.csv("sam_data.csv")
map[,confound] <- as.factor(map[,confound])
rownames(map) <- map$Biospecimen.Barcode
sample_data(filtered_ps003) <- map
filtered_ps003NAout<- prune_samples(!is.na(map[,confound]), filtered_ps003)
if(length(levels(map[,confound])) == 2){
deseqcon<-runDESeq_time_confound(filtered_ps003NAout, deseq_cut, confound = confound)
mtgcon<-run_metagenom_seq_confound(filtered_ps003NAout, 30, mtgseq_cut, confound = confound)
tmp <-c(rownames(mtgcon[[2]]), row.names(deseqcon[2][[1]]) )
} else{
mtgcon<-run_metagenom_seq_confound(filtered_ps003NAout, 30, mtgseq_cut, confound = confound)
tmp <-rownames(mtgcon[[2]])
}
affected_taxa<-c(affected_taxa, tmp)
}
affected_taxa_cat<-unique(affected_taxa)
#Testing to see if numerical variables differ in means using wilcox or t-tests
tmp<-shapiro.test(map[,num_confounds[1]])
if (tmp$p.value <= 0.05) {
res<-wilcox.test(as.formula(paste(num_confounds[1], "~ phenotype")), data=map, var.equal = FALSE)
tab<-tibble(num_confounds[1], res$p.value, "wilcox")
colnames(tab) <- c("Var", "p.value", "Type")
numerical_test_btwn_pheno <- tab
}else{
res<-t.test(as.formula(paste(num_confounds[1], "~ phenotype")), data=map)
tab<-tibble(num_confounds[1], res$p.value, "t.test")
colnames(tab) <- c("Var", "p.value", "Type")
numerical_test_btwn_pheno <- tab
}
for (i in num_confounds[c(-1)]) {
tmp<-shapiro.test(map[,i])
if (tmp$p.value <= 0.05) {
res<-wilcox.test(as.formula(paste(i, "~ phenotype")), data=map, var.equal = FALSE)
tab<-tibble(i, res$p.value, "wilcox")
colnames(tab) <- c("Var", "p.value", "Type")
numerical_test_btwn_pheno <-rbind(numerical_test_btwn_pheno, tab)
}else{
res<-t.test(as.formula(paste(i, "~ phenotype")), data=map)
tab<-tibble(i, res$p.value, "t.test")
colnames(tab) <- c("Var", "p.value", "Type")
numerical_test_btwn_pheno <-rbind(numerical_test_btwn_pheno, tab)
}
}
numerical_test_btwn_pheno$p.value<-p.adjust(numerical_test_btwn_pheno$p.value)
sig_numvar<-numerical_test_btwn_pheno[which(numerical_test_btwn_pheno$p.value <= 0.05),]
num_confounds2 <- sig_numvar$Var
#Spearman test between taxa and possible confounding variables that are ordinal
confound <- num_confounds2[1]
otus<-as.data.frame(otu_table(ps_DeSeq_norm_pass_min_postDD_sup003))
otus <- t(otus)
otus <- as.data.frame(otus)
otus$confound <- map[,confound]
form <-as.formula(paste("~", colnames(otus)[1], "+", "confound"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
speartesttab <- tibble(colnames(otus)[1], tmp$p.value)
colnames(speartesttab) <- c("otu", "p_val")
for (i in 2:(length(colnames(otus))-1)){
otus$confound <- map[,confound]
form <-as.formula(paste("~", colnames(otus)[i], "+", "confound"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
tab <- tibble(colnames(otus)[i], tmp$p.value)
colnames(tab) <- c("otu", "p_val")
speartesttab <-rbind(speartesttab, tab)
}
spear_p.val<-p.adjust(speartesttab$p_val)
spear_sig_asvs_p.val<-speartesttab$otu[spear_p.val < 0.05]
for (x in 2:length(num_confounds2)){
confound <- num_confounds2[x]
otus<-as.data.frame(otu_table(ps_DeSeq_norm_pass_min_postDD_sup003))
otus <- t(otus)
otus <- as.data.frame(otus)
otus$confound <- map[,confound]
form <-as.formula(paste("~", colnames(otus)[1], "+", "confound"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
speartesttab <- tibble(colnames(otus)[1], tmp$p.value)
colnames(speartesttab) <- c("otu", "p_val")
for (i in 2:(length(colnames(otus))-1)){
otus$confound <- map[,confound]
form <-as.formula(paste("~", colnames(otus)[i], "+", "confound"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
tab <- tibble(colnames(otus)[i], tmp$p.value)
colnames(tab) <- c("otu", "p_val")
speartesttab <-rbind(speartesttab, tab)
}
spear_p.val<-p.adjust(speartesttab$p_val)
spear_sig_asvs_p.val<-c(spear_sig_asvs_p.val, speartesttab$otu[spear_p.val < 0.05])
}
#generate full list of significant ones with confounds
affected_num_list <- unique(spear_sig_asvs_p.val)
full_confound_asv_list <-c(affected_num_list, affected_taxa_cat)
full_confound_asv_list<-unique(full_confound_asv_list)
#Filter them out from main list and save
full_sigtab_esv_confoundfiltered<-fullsigtab_esv[-which(rownames(fullsigtab_esv) %in% full_confound_asv_list), ]
write.csv(full_sigtab_esv_confoundfiltered, "Full_sig_asvs_w_confounding_var_asvs_filtered_out.csv")
confounding_var_list <-c(num_confounds2, cat_confounds)
saveRDS(confounding_var_list, "confounding_var_list.rds")
full_sigtab_esv_confoundfiltered.print <- full_sigtab_esv_confoundfiltered
rownames(full_sigtab_esv_confoundfiltered.print) <- NULL
full_sigtab_esv_confoundfiltered.print$ASV <- NULL
full_sigtab_esv_confoundfiltered.print## Method+Data Domain Phylum Class
## 1 Mtg_P1 d__Bacteria p__Actinobacteriota c__Actinobacteria
## 2 Mtg_P1 d__Bacteria p__Firmicutes c__Bacilli
## 3 Mtg_P2 d__Bacteria p__Firmicutes c__Bacilli
## 4 Mtg_P2 d__Bacteria p__unclassified c__unclassified
## 5 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 6 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 7 Mtg_P3 d__Bacteria p__Firmicutes c__Bacilli
## 8 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 9 Mtg_P3 d__Bacteria p__Firmicutes_A c__Clostridia
## 10 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 11 Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 12 Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 13 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 14 Mtg_P1Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 15 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 16 Mtg_P2Mtg_All d__Bacteria p__Firmicutes_A c__Clostridia
## 17 Mtg_P3Mtg_All d__Bacteria p__Firmicutes_C c__Negativicutes
## 18 Mtg_P3Mtg_All d__Bacteria p__Bacteroidota c__Bacteroidia
## 19 DESeq2_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 20 DESeq2_P1 d__Bacteria p__Firmicutes_A c__Clostridia
## 21 DESeq2_P1 d__Bacteria p__Firmicutes_C c__Negativicutes
## 22 DESeq2_P3 d__Bacteria p__Bacteroidota c__Bacteroidia
## 23 DESeq2_P3 d__Bacteria p__Firmicutes c__Bacilli
## 24 DESeq2_P1+P3 d__Bacteria p__Bacteroidota c__Bacteroidia
## Order Family Genus
## 1 o__Actinomycetales f__Bifidobacteriaceae g__Bifidobacterium
## 2 o__Lactobacillales f__Streptococcaceae g__Streptococcus
## 3 o__Erysipelotrichales f__Erysipelotrichaceae g__Absiella
## 4 o__unclassified f__unclassified g__unclassified
## 5 o__Lachnospirales f__Lachnospiraceae g__Coprococcus_A
## 6 o__Lachnospirales f__Lachnospiraceae g__unclassified
## 7 o__Erysipelotrichales f__Erysipelatoclostridiaceae g__Erysipelatoclostridium
## 8 o__Lachnospirales f__Lachnospiraceae g__PROV_t__256727
## 9 o__Oscillospirales f__Ruminococcaceae g__unclassified
## 10 o__Oscillospirales f__Acutalibacteraceae g__unclassified
## 11 o__unclassified f__unclassified g__unclassified
## 12 o__Bacteroidales f__Bacteroidaceae g__Bacteroides
## 13 o__Lachnospirales f__Lachnospiraceae g__Blautia_A
## 14 o__Lachnospirales f__Lachnospiraceae g__PROV_t__182732
## 15 o__Lachnospirales f__Lachnospiraceae g__unclassified
## 16 o__Lachnospirales f__Lachnospiraceae g__Eubacterium_E
## 17 o__Veillonellales f__Veillonellaceae g__Veillonella
## 18 o__Bacteroidales f__Marinifilaceae g__Odoribacter
## 19 o__Oscillospirales f__Ruminococcaceae g__Faecalibacterium
## 20 o__unclassified f__unclassified g__unclassified
## 21 o__Veillonellales f__Dialisteraceae g__Dialister
## 22 o__Bacteroidales f__Bacteroidaceae g__Bacteroides
## 23 o__Lactobacillales f__Streptococcaceae g__Lactococcus
## 24 o__Bacteroidales f__Barnesiellaceae g__Barnesiella
## Species Strain
## 1 s__Bifidobacterium__bifidum t__unclassified
## 2 s__unclassified t__unclassified
## 3 s__unclassified t__unclassified
## 4 s__unclassified t__unclassified
## 5 s__Coprococcus_A__catus t__92557
## 6 s__unclassified t__unclassified
## 7 s__unclassified t__unclassified
## 8 s__PROV_t__256727 t__256727
## 9 s__unclassified t__unclassified
## 10 s__unclassified t__unclassified
## 11 s__unclassified t__unclassified
## 12 s__unclassified t__unclassified
## 13 s__unclassified t__unclassified
## 14 s__unclassified t__unclassified
## 15 s__unclassified t__unclassified
## 16 s__Eubacterium_E__hallii_A t__unclassified
## 17 s__unclassified t__unclassified
## 18 s__Odoribacter__splanchnicus t__unclassified
## 19 s__unclassified t__unclassified
## 20 s__unclassified t__unclassified
## 21 s__PROV_t__4989 t__4989
## 22 s__Bacteroides__intestinalis t__unclassified
## 23 s__unclassified t__unclassified
## 24 s__Barnesiella__intestinihominis t__213167.4.2 Digital phenotype
sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)$Mobile.Autism.Risk.Assessment.Score <- as.numeric(sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)$Mobile.Autism.Risk.Assessment.Score)
#Spearman test for taxa correlated with MARA with Deseq
ps_DeSeq_norm_pass_min_postDD_sup003_A <- subset_samples(ps_DeSeq_norm_pass_min_postDD_sup003, phenotype == "A")
otus<-as.data.frame(otu_table(ps_DeSeq_norm_pass_min_postDD_sup003_A))
otus <- t(otus)
otus <- as.data.frame(otus)
mapa <- map[which(map$phenotype == "A"),]
otus$MARA <- mapa[,"Mobile.Autism.Risk.Assessment.Score"]
otus$ID <- mapa$Biospecimen.Barcode
form <-as.formula(paste("~", colnames(otus)[1], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
speartesttab <- tibble(colnames(otus)[1], tmp$p.value)
colnames(speartesttab) <- c("otu", "p_val")
for (i in 2:(length(colnames(otus))-2)){
otus$MARA <- mapa[,"Mobile.Autism.Risk.Assessment.Score"]
form <-as.formula(paste("~", colnames(otus)[i], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
tab <- tibble(colnames(otus)[i], tmp$p.value)
colnames(tab) <- c("otu", "p_val")
speartesttab <-rbind(speartesttab, tab)
}
spear_p.val<-p.adjust(speartesttab$p_val)
spear_sig_asvs_p.val_des_all_MARA<-speartesttab$otu[spear_p.val < 0.05]
#Spearman test for taxa correlated with MARA with CSS
ps_CSS_norm_pass_min_postDD_sup003_A <- subset_samples(ps_CSS_norm_pass_min_postDD_sup003, phenotype == "A")
otus<-as.data.frame(otu_table(ps_CSS_norm_pass_min_postDD_sup003_A))
otus <- t(otus)
otus <- as.data.frame(otus)
mapa <- map[which(map$phenotype == "A"),]
otus$MARA <- mapa[,"Mobile.Autism.Risk.Assessment.Score"]
otus$ID <- mapa$Biospecimen.Barcode
form <-as.formula(paste("~", colnames(otus)[1], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
speartesttab <- tibble(colnames(otus)[1], tmp$p.value)
colnames(speartesttab) <- c("otu", "p_val")
for (i in 2:(length(colnames(otus))-2)){
otus$MARA <- mapa[,"Mobile.Autism.Risk.Assessment.Score"]
form <-as.formula(paste("~", colnames(otus)[i], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
tab <- tibble(colnames(otus)[i], tmp$p.value)
colnames(tab) <- c("otu", "p_val")
speartesttab <-rbind(speartesttab, tab)
}
spear_p.val<-p.adjust(speartesttab$p_val)
spear_sig_asvs_p.val_des_all_MARA_CSS<-speartesttab$otu[spear_p.val < 0.05]
#Same asvs, but not found in fullsigtab_esv
#Trying by timepoint
#Timepoint 1
P1_des<-prune_samples(rownames(sample_data(ps_DeSeq_norm_pass_min_postDD_sup003_A))[sample_data(ps_DeSeq_norm_pass_min_postDD_sup003_A)$Within.study.sampling.date == "Timepoint 1"], ps_DeSeq_norm_pass_min_postDD_sup003_A)
otus<-as.data.frame(otu_table(P1_des))
otus <- t(otus)
otus <- as.data.frame(otus)
otus$MARA <- P1_des@sam_data$Mobile.Autism.Risk.Assessment.Score
form <-as.formula(paste("~", colnames(otus)[1], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
speartesttab <- tibble(colnames(otus)[1], tmp$p.value)
colnames(speartesttab) <- c("otu", "p_val")
for (i in 2:(length(colnames(otus))-1)){
otus$MARA <- P1_des@sam_data$Mobile.Autism.Risk.Assessment.Score
form <-as.formula(paste("~", colnames(otus)[i], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
tab <- tibble(colnames(otus)[i], tmp$p.value)
colnames(tab) <- c("otu", "p_val")
speartesttab <-rbind(speartesttab, tab)
}
spear_p.val<-p.adjust(speartesttab$p_val)
spear_sig_asvs_p.val_des_1_MARA<-speartesttab$otu[spear_p.val < 0.05]
#Timepoint 2
P2_des<-prune_samples(rownames(sample_data(ps_DeSeq_norm_pass_min_postDD_sup003))[sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)$Within.study.sampling.date == "Timepoint 2"], ps_DeSeq_norm_pass_min_postDD_sup003)
otus<-as.data.frame(otu_table(P2_des))
otus <- t(otus)
otus <- as.data.frame(otus)
otus$MARA <- P2_des@sam_data$Mobile.Autism.Risk.Assessment.Score
form <-as.formula(paste("~", colnames(otus)[1], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
speartesttab <- tibble(colnames(otus)[1], tmp$p.value)
colnames(speartesttab) <- c("otu", "p_val")
for (i in 2:(length(colnames(otus))-1)){
otus$MARA <- P2_des@sam_data$Mobile.Autism.Risk.Assessment.Score
form <-as.formula(paste("~", colnames(otus)[i], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
tab <- tibble(colnames(otus)[i], tmp$p.value)
colnames(tab) <- c("otu", "p_val")
speartesttab <-rbind(speartesttab, tab)
}
spear_p.val<-p.adjust(speartesttab$p_val)
spear_sig_asvs_p.val_des_2_MARA<-speartesttab$otu[spear_p.val < 0.05]
#Timepoint 3
P3_des<-prune_samples(rownames(sample_data(ps_DeSeq_norm_pass_min_postDD_sup003))[sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)$Within.study.sampling.date == "Timepoint 3"], ps_DeSeq_norm_pass_min_postDD_sup003)
otus<-as.data.frame(otu_table(P3_des))
otus <- t(otus)
otus <- as.data.frame(otus)
otus$MARA <- P3_des@sam_data$Mobile.Autism.Risk.Assessment.Score
form <-as.formula(paste("~", colnames(otus)[1], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
speartesttab <- tibble(colnames(otus)[1], tmp$p.value)
colnames(speartesttab) <- c("otu", "p_val")
for (i in 2:(length(colnames(otus))-1)){
otus$MARA <- P3_des@sam_data$Mobile.Autism.Risk.Assessment.Score
form <-as.formula(paste("~", colnames(otus)[i], "+", "MARA"))
tmp<-cor.test(formula = form, data = otus, method = "spearman",
continuity = FALSE,
conf.level = 0.95, exact = FALSE)
tab <- tibble(colnames(otus)[i], tmp$p.value)
colnames(tab) <- c("otu", "p_val")
speartesttab <-rbind(speartesttab, tab)
}
spear_p.val<-p.adjust(speartesttab$p_val)
spear_sig_asvs_p.val_des_3_MARA<-speartesttab$otu[spear_p.val < 0.05]
wo_na_pcoa(ps_DeSeq_norm_pass_min_postDD_sup003, 'Mobile.Autism.Risk.Assessment.Score', GP.ord)
7.4.3 Probiotics
wo_na_pcoa(ps_DeSeq_norm_pass_min_postDD_sup003, 'Probiotic..consumption.frequency.', GP.ord)
7.4.4 Antibiotics
# Anti.infective
wo_na_pcoa(ps_DeSeq_norm_pass_min_postDD_sup003, 'Anti.infective', GP.ord)
# Minimum.time.since.antibiotics
sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)$Minimum.time.since.antibiotics <- as.numeric(sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)$Minimum.time.since.antibiotics)
wo_na_pcoa(ps_DeSeq_norm_pass_min_postDD_sup003, 'Minimum.time.since.antibiotics', GP.ord)
7.4.5 All other passed R2 cutoff and significant
for(pvar in permanova_res[R2>permanova_cut & pvalue<permanova_pcut]$Variable){
wo_na_pcoa(ps_DeSeq_norm_pass_min_postDD_sup003, pvar, GP.ord)
}
7.4.6 Random Forest
#Random Forest main function
rand_forest <- function(pred_sequences, ps){
ps <-prune_taxa(pred_sequences, ps )
phen <- sample_data(ps)$phenotype
family <- unique(sample_data(ps)$Family.group.ID)
fam_id <- sample_data(ps)$Family.group.ID
data<-t(otu_table(ps))
data <- data.frame(phen, data, fam_id)
set.seed(103)
folds_by_family<-groupKFold(fam_id, 10)
validate <- data[-folds_by_family[[1]],]
training <- data[folds_by_family[[1]],]
validate$fam_id <- NULL
training$fam_id <- NULL
set.seed(1)
control <- trainControl(method='repeatedcv',
number=3,
repeats=3)
tunegrid <- expand.grid(.mtry=c(3:20)) #mtry is the depth of each decision tree
rf <- train(phen ~.,
data= training,
method='rf',
metric='Accuracy',
tuneGrid=tunegrid,
trControl=control)
mtry_best = as.numeric(rf$bestTune)
set.seed(1)
AR.classify <- randomForest(phen~., data = training, ntree = 128, mtry = mtry_best, importance = TRUE)
rf<-AR.classify
OOB.votes <- predict(rf,validate[,-1],type="prob");
OOBpred_votes <- OOB.votes
pred_votes <- OOBpred_votes[,2]
for(i in 2:10){
validate <- data[-folds_by_family[[i]],]
training <- data[folds_by_family[[i]],]
validate$fam_id <- NULL
training$fam_id <- NULL
set.seed(1)
control <- trainControl(method='repeatedcv',
number=3,
repeats=3)
tunegrid <- expand.grid(.mtry=c(3:20)) #mtry is the depth of each decision tree
rf <- train(phen ~.,
data= training,
method='rf',
metric='Accuracy',
tuneGrid=tunegrid,
trControl=control)
mtry_best = as.numeric(rf$bestTune)
set.seed(1)
AR.classify <- randomForest(phen~., data = training, ntree = 128, mty = mtry_best, importance = TRUE)
rf<-AR.classify
OOB.votes <- predict (rf,validate[,-1],type="prob");
OOB.votes
pred_votes<-append(pred_votes, OOB.votes[,2])
}
a<-tibble(names(pred_votes), pred_votes)
b <- a[order(a$`names(pred_votes)`), ]
data<- data[order(rownames(data)),]
pred.obj <- prediction(b$pred_votes,data$phen);
perf_AUC=performance(pred.obj,"auc") #Calculate the AUC value
AUCagp1=perf_AUC@y.values[[1]]
AUCagp1
RP.perf <- performance(pred.obj, "prec","rec");
ROC.perfAGP <- performance(pred.obj, "tpr","fpr");
outputlist <-list()
outputlist[1] <- AUCagp1
outputlist[2] <- ROC.perfAGP
return(outputlist)
}
#For 24 (set of asvs with confounds filtered out)
ROC.perf_24 <-rand_forest(pred_sequences = rownames(full_sigtab_esv_confoundfiltered), ps = ps_DeSeq_norm_pass_min_postDD_sup003)
#For 70 (unfiltered results)
ROC.perf_70 <-rand_forest(rownames(fullsigtab_esv), ps_DeSeq_norm_pass_min_postDD_sup003)
#Combine AUCs
AUC_all <- c(ROC.perf_24[1], ROC.perf_70[1])
#Plot
{plot(ROC.perf_24[[2]], main = "10-cross validation", col = rainbow(8)[1])
plot(ROC.perf_70[[2]], main = "10-cross validation", col = rainbow(8)[2], add = TRUE)
lines(c(0,1), c(0,1), col = "black")
legend(title = "AUC value", .8, .33, legend=round(as.numeric(AUC_all), digits = 4),
col=colors,cex=1.0)
predictors_name <- c("24 Diff. Abundant ASVs", "70 Diff. Abundant ASVs")
legend(title = "Predictors",.375, .33, legend=predictors_name,
col=c(rainbow(8)[c(1,2,3,5,7,6,8)], hcl.colors(1, palette = "viridis")),cex=1.0, lty=1:7)
}
rand_forest_time <- function(pred_sequences, ps){
ps <-prune_taxa(pred_sequences, ps )
phen <- sample_data(ps)$phenotype
data<-t(otu_table(ps))
data <- data.frame(phen, data)
set.seed(103)
folds<-createFolds(data$phen, k=10)
validate <- data[folds[[1]],]
training <- data[-folds[[1]],]
set.seed(1)
control <- trainControl(method='repeatedcv',
number=3,
repeats=3)
tunegrid <- expand.grid(.mtry=c(3:20)) #mtry is the depth of each decision tree
rf <- train(phen ~.,
data= training,
method='rf',
metric='Accuracy',
tuneGrid=tunegrid,
trControl=control)
mtry_best = as.numeric(rf$bestTune)
set.seed(1)
AR.classify <- randomForest(phen~., data = training, ntree = 128, mtry = mtry_best, importance = TRUE)
rf<-AR.classify
OOB.votes <- predict(rf,validate[,-1],type="prob");
OOBpred_votes <- OOB.votes
pred_votes <- OOBpred_votes[,2]
for(i in 2:10){
validate <- data[folds[[i]],]
training <- data[-folds[[i]],]
set.seed(1)
control <- trainControl(method='repeatedcv',
number=3,
repeats=3)
tunegrid <- expand.grid(.mtry=c(3:20)) #mtry is the depth of each decision tree
rf <- train(phen ~.,
data= training,
method='rf',
metric='Accuracy',
tuneGrid=tunegrid,
trControl=control)
mtry_best = as.numeric(rf$bestTune)
set.seed(1)
AR.classify <- randomForest(phen~., data = training, ntree = 128, mty = mtry_best, importance = TRUE)
rf<-AR.classify
OOB.votes <- predict (rf,validate[,-1],type="prob");
OOB.votes
pred_votes<-append(pred_votes, OOB.votes[,2])
}
a<-tibble(names(pred_votes), pred_votes)
b <- a[order(a$`names(pred_votes)`), ]
data<- data[order(rownames(data)),]
pred.obj <- prediction(b$pred_votes,data$phen);
perf_AUC=performance(pred.obj,"auc") #Calculate the AUC value
AUCagp1=perf_AUC@y.values[[1]]
AUCagp1
RP.perf <- performance(pred.obj, "prec","rec");
ROC.perfAGP <- performance(pred.obj, "tpr","fpr");
outputlist <-list()
outputlist[1] <- AUCagp1
outputlist[2] <- ROC.perfAGP
return(outputlist)
}
#Per timepoints
P1_des<-prune_samples(rownames(sample_data(ps_DeSeq_norm_pass_min_postDD_sup003))[sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)$Within.study.sampling.date == "Timepoint 1"], ps_DeSeq_norm_pass_min_postDD_sup003)
P2_des<-prune_samples(rownames(sample_data(ps_DeSeq_norm_pass_min_postDD_sup003))[sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)$Within.study.sampling.date == "Timepoint 2"], ps_DeSeq_norm_pass_min_postDD_sup003)
P3_des<-prune_samples(rownames(sample_data(ps_DeSeq_norm_pass_min_postDD_sup003))[sample_data(ps_DeSeq_norm_pass_min_postDD_sup003)$Within.study.sampling.date == "Timepoint 3"], ps_DeSeq_norm_pass_min_postDD_sup003)
ROC.perf_24_P1 <-rand_forest_time(pred_sequences = rownames(full_sigtab_esv_confoundfiltered), ps = P1_des)
ROC.perf_24_P2 <-rand_forest_time(pred_sequences = rownames(full_sigtab_esv_confoundfiltered), ps = P2_des)
ROC.perf_24_P3 <-rand_forest_time(pred_sequences = rownames(full_sigtab_esv_confoundfiltered), ps = P3_des)
AUC_all <- c(ROC.perf_24_P1[1], ROC.perf_24_P2[1], ROC.perf_24_P3[1])
{plot(ROC.perf_24_P1[[2]], main = "10-cross validation for 24 Sequences by Timepoint", col = rainbow(8)[1])
plot(ROC.perf_24_P2[[2]], main = "10-cross validation", col = rainbow(8)[2], add = TRUE)
plot(ROC.perf_24_P2[[2]], main = "10-cross validation", col = rainbow(8)[3], add = TRUE)
lines(c(0,1), c(0,1), col = "black")
legend(title = "AUC value", .8, .33, legend=round(as.numeric(AUC_all), digits = 4),
col=colors,cex=1.0)
predictors_name <- c("24 Diff. Abundant ASVs P1", "24 Diff. Abundant ASVs P2","24 Diff. Abundant ASVs P3")
legend(title = "Predictors",.375, .33, legend=predictors_name,
col=c(rainbow(8)[c(1,2,3,5,7,6,8)], hcl.colors(1, palette = "viridis")),cex=1.0, lty=1:7)
}
ROC.perf_70_P1 <-rand_forest_time(pred_sequences = rownames(fullsigtab_esv), ps = P1_des)
ROC.perf_70_P2 <-rand_forest_time(pred_sequences = rownames(fullsigtab_esv), ps = P2_des)
ROC.perf_70_P3 <-rand_forest_time(pred_sequences = rownames(fullsigtab_esv), ps = P3_des)
AUC_all <- c(ROC.perf_70_P1[1], ROC.perf_70_P2[1], ROC.perf_70_P3[1])
{plot(ROC.perf_70_P1[[2]], main = "10-cross validation for 70 Sequences by Timepoint", col = rainbow(8)[1])
plot(ROC.perf_70_P2[[2]], main = "10-cross validation", col = rainbow(8)[2], add = TRUE)
plot(ROC.perf_70_P2[[2]], main = "10-cross validation", col = rainbow(8)[3], add = TRUE)
lines(c(0,1), c(0,1), col = "black")
legend(title = "AUC value", .8, .33, legend=round(as.numeric(AUC_all), digits = 4),
col=colors,cex=1.0)
predictors_name <- c("70 Diff. Abundant ASVs P1", "70 Diff. Abundant ASVs P2","70 Diff. Abundant ASVs P3")
legend(title = "Predictors",.375, .33, legend=predictors_name,
col=c(rainbow(8)[c(1,2,3,5,7,6,8)], hcl.colors(1, palette = "viridis")),cex=1.0, lty=1:7)
}
8 Session information
sessionInfo()## R version 3.6.2 (2019-12-12)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: CentOS Linux 8 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] phyloseq_1.30.0 biomformat_1.14.0
## [3] DESeq2_1.26.0 SummarizedExperiment_1.16.1
## [5] DelayedArray_0.12.3 BiocParallel_1.20.1
## [7] matrixStats_0.56.0 GenomicRanges_1.38.0
## [9] GenomeInfoDb_1.22.1 IRanges_2.20.2
## [11] S4Vectors_0.24.4 metagenomeSeq_1.28.2
## [13] RColorBrewer_1.1-2 glmnet_4.0-2
## [15] Matrix_1.2-18 limma_3.42.2
## [17] Biobase_2.46.0 BiocGenerics_0.32.0
## [19] vegan_2.5-6 permute_0.9-5
## [21] ggpubr_0.4.0 compositions_2.0-0
## [23] nlme_3.1-148 exactRankTests_0.8-31
## [25] ROCR_1.0-11 randomForest_4.6-14
## [27] caret_6.0-86 lattice_0.20-41
## [29] smatr_3.4-8 adegraphics_1.0-15
## [31] gridExtra_2.3 DT_0.14
## [33] pander_0.6.3 ggplot2_3.3.2
## [35] dplyr_1.0.0 reshape2_1.4.4
## [37] tidyr_1.1.0 knitr_1.29
## [39] devtools_2.3.1 usethis_1.6.1
## [41] data.table_1.13.0 tibble_3.0.2
## [43] shiny_1.5.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.1.0 RSQLite_2.2.0 AnnotationDbi_1.48.0
## [4] htmlwidgets_1.5.1 grid_3.6.2 pROC_1.16.2
## [7] IHW_1.14.0 munsell_0.5.0 codetools_0.2-16
## [10] withr_2.2.0 colorspace_1.4-1 rstudioapi_0.11
## [13] robustbase_0.93-6 bayesm_3.1-4 ggsignif_0.6.0
## [16] labeling_0.3 slam_0.1-47 GenomeInfoDbData_1.2.2
## [19] lpsymphony_1.16.0 farver_2.0.3 bit64_0.9-7
## [22] rhdf5_2.30.1 rprojroot_1.3-2 vctrs_0.3.1
## [25] generics_0.0.2 ipred_0.9-9 xfun_0.16
## [28] R6_2.4.1 locfit_1.5-9.4 bitops_1.0-6
## [31] assertthat_0.2.1 promises_1.1.1 scales_1.1.1
## [34] nnet_7.3-14 gtable_0.3.0 processx_3.4.3
## [37] timeDate_3043.102 rlang_0.4.6 genefilter_1.68.0
## [40] splines_3.6.2 rstatix_0.6.0 ModelMetrics_1.2.2.2
## [43] acepack_1.4.1 broom_0.7.0 checkmate_2.0.0
## [46] yaml_2.2.1 abind_1.4-5 crosstalk_1.1.0.1
## [49] backports_1.1.8 httpuv_1.5.4 Hmisc_4.4-0
## [52] tensorA_0.36.1 tools_3.6.2 lava_1.6.7
## [55] ellipsis_0.3.1 gplots_3.0.4 sessioninfo_1.1.1
## [58] Rcpp_1.0.5 plyr_1.8.6 base64enc_0.1-3
## [61] zlibbioc_1.32.0 purrr_0.3.4 RCurl_1.98-1.2
## [64] ps_1.3.3 prettyunits_1.1.1 rpart_4.1-15
## [67] Wrench_1.4.0 haven_2.3.1 cluster_2.1.0
## [70] fs_1.4.2 magrittr_1.5 openxlsx_4.1.5
## [73] pkgload_1.1.0 hms_0.5.3 mime_0.9
## [76] evaluate_0.14 xtable_1.8-4 XML_3.99-0.3
## [79] rio_0.5.16 jpeg_0.1-8.1 readxl_1.3.1
## [82] shape_1.4.4 testthat_2.3.2 compiler_3.6.2
## [85] KernSmooth_2.23-17 crayon_1.3.4 htmltools_0.5.0
## [88] mgcv_1.8-31 later_1.1.0.1 Formula_1.2-3
## [91] geneplotter_1.64.0 DBI_1.1.0 lubridate_1.7.9
## [94] MASS_7.3-51.6 ade4_1.7-15 car_3.0-8
## [97] cli_2.0.2 gdata_2.18.0 igraph_1.2.5
## [100] gower_0.2.2 forcats_0.5.0 pkgconfig_2.0.3
## [103] foreign_0.8-76 sp_1.4-2 recipes_0.1.13
## [106] foreach_1.5.0 annotate_1.64.0 multtest_2.42.0
## [109] XVector_0.26.0 prodlim_2019.11.13 stringr_1.4.0
## [112] callr_3.4.3 digest_0.6.25 Biostrings_2.54.0
## [115] rmarkdown_2.3 cellranger_1.1.0 htmlTable_2.0.1
## [118] curl_4.3 gtools_3.8.2 jsonlite_1.7.0
## [121] lifecycle_0.2.0 Rhdf5lib_1.8.0 carData_3.0-4
## [124] desc_1.2.0 fansi_0.4.1 pillar_1.4.6
## [127] fastmap_1.0.1 DEoptimR_1.0-8 pkgbuild_1.1.0
## [130] survival_3.2-3 glue_1.4.1 remotes_2.2.0
## [133] zip_2.0.4 fdrtool_1.2.15 png_0.1-7
## [136] iterators_1.0.12 bit_1.1-15.2 class_7.3-17
## [139] stringi_1.4.6 blob_1.2.1 latticeExtra_0.6-29
## [142] caTools_1.18.0 memoise_1.1.0 e1071_1.7-3
## [145] ape_5.4